Modeling the Emergence of Echinochloa sp. in Flooded Rice Systems

Abstract

Barnyard grass (Echinochloa sp.) is one of the main rice weeds. Knowledge of its emergence can support management measures. The present study models barnyard grass emergence at different flooded rice sowing periods. Furthermore, the effectiveness of the hydrothermal time model in estimating barnyard grass emergence is analyzed. Field emergence was monitored three times a week during two sowing times (October and November) and two growing seasons (2017/18 and 2018/19), in rice sown and unsown areas. Data were converted to cumulative emergence based on total seedlings. Soil temperature and moisture data were used to determine hydrothermal time. The sowing in October led to a continuous emergence of barnyard grass, while the sowing in late November led to different emergence rates. The highest emergence rates of barnyard grass occur in the first sowing time. The hydrothermal time model is adequate to estimate barnyard grass emergence in both sowing times.

Emergence modeling of three‐lobe morning glory (Ipomoea triloba L.) and demographic parameters of seed longevity and persistence in the soil

“…Therefore, the development of the emergence model based on thermal and hydrothermal time were favored, and both models described the emergence of three‐lobe morning glory in the three sampling times using the Weibull sigmoidal function with four parameters (Figure 3). This result is of great importance, since these models are rarely used in tropical environments (Goulart et al, 2020; Werle et al, 2014), and the successful prediction of seedling emergence of three‐lobe morning glory at tropical conditions could help to fill this knowledge gap.…”

“…Soil moisture in the 0-5 cm layer was determined every 4 days, concomitantly with the emergency counts. The soil was collected with an auger (with dimensions of 5.0 cm in height by 5.0 cm in diameter, area 98.17 cm 3 ), weighed and taken to the oven for drying, later weighed again, with soil moisture obtained by subtracting the mass of the soil at the time of collection and the dry soil (Goulart et al, 2020). The collection range corresponds to the layer where more than 90% of weed emergence occurs (Monquero & Christoffoleti, 2005).…”

“…On the other hand, empirical models require less data and can offer the simplicity and flexibility needed for decision making on weed control. Thus, various types of empirical models have been used to predict weed emergence based on thermal time or hydrothermal time (Dorado et al, 2009; Ledda et al, 2022; Masin et al, 2014; Werle et al, 2014), but few are developed for species in tropical environments (Goulart et al, 2020). There is a gap of weed seedling emergence models adapted for tropical environments, where air temperature and rainfall during year seasons are well defined (Köppen, 1990).…”

Features of germination, emergence modeling, longevity, and persistence in Bidens pilosa seed bank

“…During the experiment, daily measurements of soil and ambient temperature were carried out in the experimental area with the aid of a data logger (model Pro v2 2x External Temperature, manufacturer Onset HOBO®). Soil moisture in the zero-to ve-cm layer was determined every four days, at the same time as the emergence counts (Goulart et al 2020). The soil was collected with a sampling spear, weighed, and taken to the oven for drying, and later weighed again, with soil moisture obtained by subtracting the mass of the soil at the time of collection and the dry soil.…”