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<p>The present study examined the influence of short-term climatic variability on the occurrence and cover of invasive weed species in Hungarian agroecosystems between 2017 and 2020. A nationwide weed survey was conducted in 191 municipalities to record weed cover in major crops and selected habitat types. The relationship between gridded meteorological data on temperature and precipitation and weed cover patterns was analysed. The findings, based on one-way and two-way ANOVA, in conjunction with principal component analysis (PCA), indicated that year and weed species were the most significant predictors of weed cover. Higher infestation levels were found to be associated with climatic anomalies, particularly elevated temperatures in March (ΔT ≈ + 3&#xa0;°C) and increased precipitation in May (e.g. +213% in 2019) and June (e.g. +132% in 2018). The findings of this study suggest that interannual climatic variability can significantly influence the dynamics of invasive weeds within agroecosystems. The present study provides multi-year, field-based evidence from Hungary that can support climate-informed weed monitoring, risk assessment and adaptive weed management under changing environmental conditions in Central Europe. The present study examined the correlation between climatic variability and the extent of invasive weed cover within Hungarian agroecosystems during the period 2017–2020. Surveys were conducted at 191 sites to record the extent of cover of invasive species in different crops and habitats. Meteorological data on temperature and precipitation were analysed using the analysis of variance (ANOVA) and principal component analysis (PCA). The analysis revealed that year and weed species were the strongest predictors of weed cover, and the highest infestation levels were observed in 2019 under warmer and wetter conditions. The findings indicate that short-term climatic variability exerts a significant influence on the dynamics of invasive weeds. The present study provides field-based evidence for Central Europe and highlights the value of integrating climate data into weed monitoring and adaptive management.</p>

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Dominant non-native weeds in different crops and their response to some abiotic factors in Hungary

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月15日

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  • Gabriella Kazinczi,
  • Eszter Schöphen,
  • Ferenc Pál-Fám,
  • Katalin Somfalvi-Tóth

摘要

The present study examined the influence of short-term climatic variability on the occurrence and cover of invasive weed species in Hungarian agroecosystems between 2017 and 2020. A nationwide weed survey was conducted in 191 municipalities to record weed cover in major crops and selected habitat types. The relationship between gridded meteorological data on temperature and precipitation and weed cover patterns was analysed. The findings, based on one-way and two-way ANOVA, in conjunction with principal component analysis (PCA), indicated that year and weed species were the most significant predictors of weed cover. Higher infestation levels were found to be associated with climatic anomalies, particularly elevated temperatures in March (ΔT ≈ + 3 °C) and increased precipitation in May (e.g. +213% in 2019) and June (e.g. +132% in 2018). The findings of this study suggest that interannual climatic variability can significantly influence the dynamics of invasive weeds within agroecosystems. The present study provides multi-year, field-based evidence from Hungary that can support climate-informed weed monitoring, risk assessment and adaptive weed management under changing environmental conditions in Central Europe. The present study examined the correlation between climatic variability and the extent of invasive weed cover within Hungarian agroecosystems during the period 2017–2020. Surveys were conducted at 191 sites to record the extent of cover of invasive species in different crops and habitats. Meteorological data on temperature and precipitation were analysed using the analysis of variance (ANOVA) and principal component analysis (PCA). The analysis revealed that year and weed species were the strongest predictors of weed cover, and the highest infestation levels were observed in 2019 under warmer and wetter conditions. The findings indicate that short-term climatic variability exerts a significant influence on the dynamics of invasive weeds. The present study provides field-based evidence for Central Europe and highlights the value of integrating climate data into weed monitoring and adaptive management.