Temperature and Water Potential as Parameters for Modeling Weed Emergence in Central-Northern Italy

Abstract

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

Base temperatures for germination of selected weed species in Iran

“…The range of t b values estimated for a. theophrasti (3.5 ± 1.15°C to 4.9 ± 0.16°C) was in complete agreement with the results of previous studies on an Iranian population (Sadeghloo et al 2013), but also on European populations (Masin et al 2010;Loddo et al 2013), while Dorado et al (2009b) reported a value of around 7°C as t b for a Spanish population. Results obtained for a. albus (t b from 10.4 ± 0.42°C to 13.1 ± 0.46°C) confirmed previous findings by Steckel et al (2004), who observed almost no germination for this species at 10°C, while higher values of t b , ranging from 14°C to 17°C, were estimated by Steinmaus et al (2000).…”

“…This procedure ensured the photostimulation necessary to promote seed germination since the studied species, as many other weeds, are very sensitive even to short light exposures as that occur during soil tillage (Batlla & Benech-Arnold 2014). Seeds were incubated at a series of constant temperatures (6, 8, 10, 12, 14, 16, 18, 20, 24, 28, and 32°C), chosen according to previous experiments conducted on different species (Steinmaus et al 2000;Steckel et al 2004;Masin et al 2010). These temperatures are within the range of suboptimal temperatures for germination of the studied species.…”

“…This approach, originally developed by Rochè et al (1997), has already been adopted to estimate t b for seed germination or rhizome sprouting (Masin et al 2010;Loddo et al 2012). A logistic function (Eq.…”

“…These models are often based on the hydrothermal time concept (Bradford 2002) and require the estimation of biological parameters, base temperature, and base water potential for germination (t b and Y b hereinafter), to simulate seedling emergence according to weather trends. Adopting inaccurate values of t b and Y b could notably influence the precision of model prediction, so several procedures have been proposed to estimate these parameters, such as linear or nonlinear regression with resampling methods (Masin et al 2010;Onofri et al 2014), population-based threshold models (Dorado et al 2009b), probit analysis (Zambrano-Navea et al 2013) or survival analysis (Onofri et al 2010).…”

“…(1) logistic model of germination time course plus linear regression of germination rate against incubation temperature with resampling bootstrap methods (Masin et al 2010), (2) probit analysis Zambrano-Navea et al 2013) were adopted independently, starting with the same germination data, to calculate t b of all studied species in order to assess potential differences between the results obtained by the two procedures.…”