Predicting weed emergence dynamics can help farmers to plan more effective weed control. The hydrothermal time concept has been used to model emergence as a function of temperature and water potential. Application of this concept is possible if the specific biological thresholds are known. This article provides a data set of base temperature and water potential of eight maize weeds (velvetleaf, redroot pigweed, common lambsquarters, large crabgrass, barnyardgrass, yellow foxtail, green foxtail, and johnsongrass). For five of these species, two ecotypes from two extreme regions of the predominant maize-growing area in Italy (Veneto and Tuscany), were collected and compared to check possible differences that may arise from using the same thresholds for different populations. Seedling emergence of velvetleaf and johnsongrass were modeled using three different approaches: (1) thermal time calculated assuming 5 C as base temperature for both species; (2) thermal time using the specific estimated base temperatures; and (3) hydrothermal time using the specific, estimated base temperatures and water potentials. All the species had base temperatures greater than 10 C, with the exception of velvetleaf (3.9 to 4.4 C) and common lambsquarters (2.0 to 2.6 C). All species showed a calculated base-water potential equal or up to 21.00 MPa. The thresholds of the two ecotypes were similar for all the studied species, with the exception of redroot pigweed, for which the Veneto ecotype showed a water potential lower than 20.41 MPa, whereas it was 20.62 MPa for the Tuscany ecotype. Similar thresholds have been found to be useful in hydrothermal time models covering two climatic regions where maize is grown in Italy. Furthermore, a comparison between the use of specific, estimated, and common thresholds for modeling weed emergence showed that, for a better determination of weed control timing, it is often necessary to estimate the specific thresholds

Digitaria sanguinalis, Eleusine indica, Setaria glauca and S. viridis are troublesome summer annual weeds in turf. For taking rational decisions on the necessity for the level and type of weed management, it is important to know when weeds are ready to emerge (dormancy status) and also how long weed seeds can survive in the soil. Seeds of these four species were buried 4.0-4.5 cm deep in steel mesh net bags placed under permanent turf and periodically exhumed for 3 years to evaluate viability and determine the dormancy/non-dormancy cycle. D. sanguinalis, S. glauca and S. viridis showed the typical dormancy cycle of summer annual species, and their seed viability declined completely after 3 years of burial. In contrast, E. indica demonstrated unusual behaviour, with long persistence and no dormancy.

Predicting weed emergence is useful for planning weed management programs. Unfortunately, our ability to anticipate initial emergence and subsequent levels of emergence from simple field observations or weather reports is often inadequate to achieve optimal control. Weed emergence models may provide predictive tools that help managers anticipate best management options and times and, thereby, improve weed control. In this study, the germination characteristics of four annual grass weeds (large crabgrass, goosegrass, green foxtail, and yellow foxtail) were investigated under different temperatures and water stresses to calculate base temperatures and base water potentials. These parameters were used to develop a mathematical model describing seedling emergence processes in terms of hydrothermal time. Hydrothermal time describes seed germination in a single equation by considering the interaction of soil water potential and soil temperature. The model, called WeedTurf, predicted emergence with some accuracy, especially for large crabgrass (lowest efficiency index [EF] value 0.95) and green foxtail (lowest EF value 0.91). These results suggest the possibility of developing interactive computer software to determine the critical timing of weed removal and provide improved recommendations for herbicide application timing.

A hydrothermal time model was developed to simulate field emergence for three weed species in maize (common lambsquarters, johnsongrass, and velvetleaf). Models predicting weed emergence facilitate well-timed and efficient POST weed control strategies (e.g., chemical and mechanical control methods). The model, called AlertInf, was created by monitoring seedling emergence from 2002 to 2008 in field experiments at three sites located in the Veneto region in northeastern Italy. Hydrothermal time was calculated using threshold parameters of temperature and water potential for germination estimated in previous laboratory studies with seeds of populations collected in Veneto. AlertInf was validated with datasets from independent field experiments conducted in Veneto and in Tuscany (west central Italy). Model validation resulted in both sites in efficiency index values ranging from 0.96 to 0.99. AlertInf, based on parameters estimated in a single region, was able to predict the timing of emergence in several sites located at the two extremes of the Italian maize growing area.

AlertInf is a recently developed model to predict the daily emergence of three important weed species in maize cropped in northern Italy (common lambsquarters, johnsongrass, and velvetleaf). Its use can improve the effectiveness and sustainability of weed control, and there has been growing interest from farmers and advisors. However, there are two important limits to its use: the low number of weed species included and its applicability only to maize. Consequently, the aim of this study was to expand the AlertInf weed list and extend its use to soybean. The first objective was to add another two important weed species for spring-summer crops in Italy, barnyardgrass and large crabgrass. Given that maize and soybean have different canopy architectures that can influence the interrow microclimate, the second objective was to compare weed emergence in maize and soybean sown on the same date. The third objective was to evaluate if AlertInf was transferable to soybean without recalibration, thus saving time and money. Results showed that predictions made by AlertInf for all five species simulated in soybean were satisfactory, as shown by the high efficiency index (EF) values, and acceptable from a practical point of view. The fact that the algorithm used for estimating weed emergence in maize was also efficient for soybean, at least for crops grown in northeastern Italy with standard cultural practices, encourages further development of AlertInf and the spread of its use.