Predicting junglerice (Echinochloa colonaL.) emergence as a function of thermal time in the humid pampas of Argentina

Junglerice (Echinochloa colona) is one of the most important annual weeds affecting crops in Argentina. A predictive seedling emergence model based on thermal time was developed and validated. Monitoring of seedling emergence was performed weekly during the growing season in a soybean field over four years. Cumulative thermal time, expressed in growing degree days (GDD), was used as the independent variable for predicting cumulative emergence. The variations in mean air temperature between late August and early September have determined a period with a conserved pattern over the years. That period had a close linear relationship (r(2)= 0.99) with the beginning of seedling emergence. A double-logistic model fitted junglerice seedling emergence better than Gompertz, Logistic or Weibull functions. Model validation showed a good performance in predicting the seedling emergence (r(2)= 0.99). Based on findings of this study it is possible to predict junglerice emergence by air temperature and, thus, to contribute reliably to the rational management of this weed.

Automated summary

A predictive seedling emergence model based on thermal time was developed and validated for Junglerice, an important annual weed affecting crops in Argentina. The study found that the variations in mean air temperature between late August and early September have a close linear relationship with the beginning of seedling emergence, with a double-logistic model fitting Junglerice seedling emergence better than other functions. Model validation showed high performance in predicting seedling emergence, indicating the potential to contribute to rational weed management.