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<p>The present work aimed to model the kernel dry matter accumulation in maize (<i>Zea mays</i> L.) hybrids across two different environments. The Weibull, Logistic, and Gompertz sigmoidal growth models were fitted to actual growth data and their predictions were compared. The Weibull model showed the best fit for the first growing season, while the Logistic model outperformed others in the second. Estimated maximum dry matter accumulation (ASYM), time in GDD at which the maximum growth rate occurs (IP) and relative rate of dry matter accumulation (GR) were calculated and compared between hybrids and years. In 2022, the maximum ASYM was noted in hybrid NS3023 (0.291&#xa0;g). In 2023, ASYM was the highest in hybrid NS6061 (0.339&#xa0;g), which also required most GDD to reach the AGR (1220.978 GDD). Significant correlations were observed between ASYM and AGR and IP and AGRmax.This study presents new findings that enhance our understanding of the selection of appropriate mathematical and statistical models for accurately describing dry matter accumulation in maize. By employing advanced modelling techniques and integrating comprehensive data analyses, this research not only refines our predictive capabilities regarding maize growth but also lays the groundwork for more targeted breeding strategies. Such strategies can potentially lead to the development of maize hybrids that are more resilient, productive, and adaptable to fluctuating environmental conditions, thereby contributing to global food security and sustainable agricultural practices.</p>

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Analysing growth dynamics: non-linear models of maize kernel dry matter accumulation

  • 期刊论文

  • 发表日期：

    2025年5月16日

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  • Maja Tanasković,
  • Velimir Mladenov,
  • Goran Bekavac,
  • Božana Purar,
  • Milan Mirosavljević,
  • Dragan Živančev,
  • Ljiljana Brbaklić,
  • Sanja Mikić,
  • Aleksandra Stankov Petreš,
  • Anja Ðurić,
  • Dušan Dunđerski

摘要

The present work aimed to model the kernel dry matter accumulation in maize (Zea mays L.) hybrids across two different environments. The Weibull, Logistic, and Gompertz sigmoidal growth models were fitted to actual growth data and their predictions were compared. The Weibull model showed the best fit for the first growing season, while the Logistic model outperformed others in the second. Estimated maximum dry matter accumulation (ASYM), time in GDD at which the maximum growth rate occurs (IP) and relative rate of dry matter accumulation (GR) were calculated and compared between hybrids and years. In 2022, the maximum ASYM was noted in hybrid NS3023 (0.291 g). In 2023, ASYM was the highest in hybrid NS6061 (0.339 g), which also required most GDD to reach the AGR (1220.978 GDD). Significant correlations were observed between ASYM and AGR and IP and AGRmax.This study presents new findings that enhance our understanding of the selection of appropriate mathematical and statistical models for accurately describing dry matter accumulation in maize. By employing advanced modelling techniques and integrating comprehensive data analyses, this research not only refines our predictive capabilities regarding maize growth but also lays the groundwork for more targeted breeding strategies. Such strategies can potentially lead to the development of maize hybrids that are more resilient, productive, and adaptable to fluctuating environmental conditions, thereby contributing to global food security and sustainable agricultural practices.