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<p>The alfalfa-production areas of Argentina are being affected by the viral disease known as alfalfa dwarf disease (ADD) which causes a detrimental effect on the forage crop and thus on bovine meat and milk production, one of the main economic activities of the country. In this work, predictive functions for ADD presence on alfalfa in Argentina were obtained with the meteorological variables that best explain ADD occurrence in spring and summer seasons. The predictive function for the occurrence of ADD in summer, developed using data from 39 sites, includes wind speed as a predictive meteorological variable, while the predictive function for the occurrence of ADD in spring, based on data from 58 sites, includes relative humidity and precipitation as predictive variables. This work provides the first predictive models regarding the occurrence of ADD in alfalfa crop based on the meteorological factors. Nevertheless, comprehending the influence of meteorological conditions on ADD and its effects on crop production remains a demanding but crucial task.</p>

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Occurrence of alfalfa dwarf disease in Argentina by a predictive function based on meteorological factors

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Veronica Trucco,
  • Franco Suarez,
  • Onias Castellanos Collazo,
  • Dariel Cabrera Mederos,
  • Monica Piccardi,
  • Fabian Giolitti

摘要

The alfalfa-production areas of Argentina are being affected by the viral disease known as alfalfa dwarf disease (ADD) which causes a detrimental effect on the forage crop and thus on bovine meat and milk production, one of the main economic activities of the country. In this work, predictive functions for ADD presence on alfalfa in Argentina were obtained with the meteorological variables that best explain ADD occurrence in spring and summer seasons. The predictive function for the occurrence of ADD in summer, developed using data from 39 sites, includes wind speed as a predictive meteorological variable, while the predictive function for the occurrence of ADD in spring, based on data from 58 sites, includes relative humidity and precipitation as predictive variables. This work provides the first predictive models regarding the occurrence of ADD in alfalfa crop based on the meteorological factors. Nevertheless, comprehending the influence of meteorological conditions on ADD and its effects on crop production remains a demanding but crucial task.