Detecting and Explaining Long-Term Trends in a Weed Community in a Biennial Cereal-Legume Rotation

The technique of Dynamic Factor Analysis (DFA), which aims to reduce the dimensionality of time series data, was utilized in order to model the changes over time in 8 different weed long-time series (26 years) growing in a biennial cereal-legume rotation. A common trend was extracted that captured the main signal of abundance over the time, indicating latent influences affecting all species. Canonical correlation analysis showed strong associations between the common trend and specific weed species, suggesting differential responses to this latent factor. Local (temperature and precipitations) and global weather factors (North Atlantic Oscillation (NAO)) were considered as explanatory variables to explain the common trend. The local weather variables considered did not play a significant role in explaining the common observed trend. Conversely, NAO showed a significant relationship with the weed community, indicating its potential role in shaping long-term weed dynamics. DFA was found to be useful for studying the variability in multivariate weed time series without the need for detailed a priori detailed information on the underlying mechanisms governing weed population dynamics. Overall, this study provides valuable insights into the long-term drivers of weed dynamics and sets the stage for future research in this area.