Wild radish (Raphanus raphanistrum L.) is a weed found globally in agricultural systems. The facultative winter annual nature of this plant and high genetic variability makes modeling its growth and phenology difficult. In the present study, R. raphanistrum natural seedbanks exhibited a biphasic pattern of emergence, with emergence peaks occurring in both fall and spring. Traditional sigmoidal models were inadequate to fit this pattern, regardless of the predictive environmental variable, and a corresponding biphasic model (sigmoidal + Weibull) was used to describe emergence based on the best parameters. Each best-fit chronological, thermal, and hydrothermal model accounted for at least 85% of the variation of the validation data. Observations on phenology progression from four cohorts were used to create a common model that described all cohorts adequately. Different phenological stages were described using chronological, thermal, hydrothermal, daylength-dependent thermal time, and daylength-dependent hydrothermal time. Integrating daylength and temperature into the models was important for predicting reproductive stages of R. raphanistrum.

Spring tillage is a component of an integrated weed management strategy for control of early emerging glyphosate-resistant weeds such as common ragweed; however, the effect of tillage on common ragweed emergence pattern is unknown. The objectives of this study were to evaluate whether spring tillage during emergence would influence the emergence pattern or stimulate additional emergence of common ragweed and to characterize common ragweed emergence in southeast Nebraska. A field experiment was conducted for three years (2014 to 2016) in Gage County, Nebraska in a field naturally infested with glyphosate-resistant common ragweed. Treatments consisted of a no-tillage control and three spring tillage timings. The Soil Temperature and Moisture Model (STM2) software was used to estimate soil temperature and moisture at a 2-cm depth. The Weibull function was fit to total common ragweed emergence (%) with day of year (DOY), thermal time, and hydrothermal time as independent variables. Tillage treatments and year had no effect on total common ragweed emergence (P=0.88 and 0.35, respectively) and time to 10, 25, 50, 75, and 90% emergence (P=0.31). However, emergence pattern was affected by year (P=<0.001) with 50% total emergence reached on May 5 in 2014, April 20 in 2015, and April 2 in 2016 and 90% total emergence reached on May 12, 2014, May 8, 2015, and April 30, 2016. According to the corrected information-theoretic model comparison criterion (AICc), the Weibull function with thermal time and base temperature of 3 C best explained the emergence pattern over three years. This study concludes that spring tillage does not stimulate additional emergence; therefore, after the majority of the common ragweed has emerged and before the crop has been planted, tillage could be used as an effective component of an integrated glyphosate-resistant common ragweed management program in Nebraska.

Laboratory studies were conducted to describe germination and seedling elongation of Ambrosia artemisiifolia L. (common ragweed) seed. The germination process was tested for the interaction of temperature and water potential across eight thermo-periods (7.5, 12.5, 17.5, 22.5, 27.5, 32.5, 37.5, and 42.5 C) and 12 water potentials (0, −0.03, −0.06, −0.1, −0.2, −0.4, −0.6, −0.9, −1.2, −1.5, −1.8, and −2.1 mPa). The rate of seedling shoot and radicle elongation was described as a function of temperature and tested for eight day: night temperature treatments (10: 5, 15 : 10, 20 : 15, 25 : 20, 30 : 25, 35 : 30, 40 : 35, and 45 : 40 C). The rate of germination was mathematically modeled by a Weibull function. Probit analysis was used to determine the cardinal temperatures (base, optimum, and maximum) and base water potential (αb ). The base temperature (T b), optimum temperature (T opt), maximum temperature (T max), and αb for A. artemisiifolia germination were estimated as 3.6, 30.9, and 40 C and −0.8 mPa, respectively. The rates of shoot and radicle elongation were described by regression models. The T b , T opt, and T max for shoot and radicle elongation were estimated as 7.7 and 5.1, 29.5 and 31.4, and 43.0 and 44.3 C, respectively. A mathematical model describing the process of A. artemisiifolia seed germination in terms of hydrothermal time (θHT) was derived. The θHT model described the phenology of A. artemisiifolia seed germination using a single curve generated from the relationship of temperature and water potential. This model can help in predicting germination and emergence of A. artemisiifolia under field conditions.

This study investigated the induction and loss of dormancy in oilseed rape (Brassica napus). Twenty genotypes were preliminary screened; from these, two genotypes, RGS003 and Hayola 308, which possess high potential for dormancy induction (HSD) and medium potential to induce secondary dormancy (MSD), were selected. The stratification of seeds at alternating temperatures of 5–30°C (in dark) significantly relieved secondary dormancy, but dormancy was not fully released. The ψb(50) values were −1.05 and −1.06 MPa for the MSD and the HSD before dormancy induction. After inducing dormancy, the ψb(50) values for the MSD and the HSD were increased to −0.59 and −0.01 on day 0 stratification at 20°C. The hydrothermal time (θHT) value was low for one-day stratification for HSD in comparison with other stratification treatments. Water stress can induce dormancy (if the seeds have the genetic potential for secondary dormancy) and warm stratification (in dark) can only reduce the intensity of dormancy. The seeds with a high potential of dormancy induction can overcome dormancy at alternating temperatures and in the presence of light. It can, therefore, be concluded that a portion of seeds can enter the cycle of dormancy ↔ non-dormancy. The secondary dormant seeds of B. napus cannot become non-dormant in darkness, but the level of dormancy may change from maximum (after water stress) to minimum (after warm stratification). It seems that the dormancy imposed by the conditions of deep burial (darkness in combination with water stress and more constant temperatures) might be more important to seed persistence than secondary dormancy induction and release. The dormancy cycle is an important pre-requisite in order to sense the depth of burial and the best time for seed germination.

Multiyear field data from Spain were used to model seedling emergence for three bedstraw species (Galium) that can coexist in winter cereal fields. The relationships between cumulative emergence and both growing degree days (GDD) and hydrothermal time (HTT) in soil were analyzed as sigmoid growth functions (Weibull). Iterations of base temperature and base water potential were used to optimize the HTT scale. All species were well described with Weibull functions. Both GDD and HTT models provided good descriptions of catchweed bedstraw emergence, as its seedlings have less dependence on soil water potential than false cleavers and threehorn bedstraw, which were described best with HTT. The HTT model for catchweed bedstraw was validated successfully with independent data from the United Kingdom. The models may be useful for predicting bedstraw emergence in semiarid Mediterranean regions and elsewhere.

Barnyardgrass is one of the most problematic weeds in Arkansas, and with the documentation of herbicide-resistant biotypes, there is a need to gain a detailed understanding of its ecology. In particular, knowledge on barnyardgrass seedbank size and emergence pattern is vital. An extensive seedbank survey was carried out in 2008 in 12 counties in eastern Arkansas to determine barnyardgrass seedbank size across the region. There was a great variability in seedbank size with a maximum of 215,000 seeds m−2. Among the fields surveyed, barnyardgrass seedbank was found only in 7% of the cotton fields, while it was 22 and 20%, respectively, for rice and soybean. To examine the emergence pattern of barnyardgrass, experiments were conducted in Rohwer (two sites), Stuttgart (one site), and Fayetteville (one site), Arkansas in 2008 and 2009. In each site, barnyardgrass emergence was quantified from naturally occurring seedbanks. Barnyardgrass exhibited an extended period of emergence with days to 100% emergence ranging from 99 to 165 across sites and years. Nevertheless, effective management may be achieved by targeting the peak emergence periods, which range from mid-April to mid-June in Arkansas. The four-parameter Weibull model provided a better fit to the cumulative emergence data. However, the thermal time (growing degree days, GDDs) or hydrothermal time (HTT) models did not predict barnyardgrass emergence any better than calendar days, perhaps because of the inherent variations associated with natural seedbanks. This study establishes seedbank size and general emergence pattern for barnyardgrass in Arkansas. Additionally, these results will be useful for parameterizing herbicide-resistance simulation models for barnyardgrass.

Weed seedbanks have been studied intensively at local scales, but to date, there have been no regional-scale studies of weed seedbank persistence. Empirical and modeling studies indicate that reducing weed seedbank persistence can play an important role in integrated weed management. Annual seedbank persistence of 13 summer annual weed species was studied from 2001 through 2003 at eight locations in the north central United States and one location in the northwestern United States. Effects of seed depth placement, tillage, and abiotic environmental factors on seedbank persistence were examined through regression and multivariate ordinations. All species examined showed a negative relationship between hydrothermal time and seedbank persistence. Seedbank persistence was very similar between the two years of the study for common lambsquarters, giant foxtail, and velvetleaf when data were pooled over location, depth, and tillage. Seedbank persistence of common lambsquarters, giant foxtail, and velvetleaf from October 2001 through 2002 and October 2002 through 2003 was, respectively, 52.3% and 60.0%, 21.3% and 21.8%, and 57.5% and 57.2%. These results demonstrate that robust estimates of seedbank persistence are possible when many observations are averaged over numerous locations. Future studies are needed to develop methods of reducing seedbank persistence, especially for weed species with particularly long-lived seeds.

Experiments were conducted in Sri Lanka to compare weed seedling emergence in three sugarcane plots of 0.1 ha planted in October 1995, January 1996, and April 1996. In each plot, weed seedling emergence was monitored for 20 wk in five permanent quadrats on each of three microsites: on ridges, in furrows, and on adjacent fallow land. Soil moisture (0 to 5 cm) and soil temperature (at 2.5 cm) were also recorded. Only crowfootgrass, swamp millet, and guineagrass (all grasses) occurred in all nine planting time-by-microsite combinations. About half of all seedlings emerging over the three planting times were swamp millet, and the next most frequent species was tropic ageratum. The composition of the emerged flora was similar on ridges and in furrows, but more seedlings emerged in the furrows than on the ridges. The highest number of emerged seedlings and of species occurred on adjacent fallow land. The major factor influencing seedling emergence appeared to be soil moisture.

The timing of weed seedling emergence relative to the crop is important in planning and optimizing the time of weed control, but very little work has been done to predict seedling emergence of tropical weed species, especially in low-input and small-scale farms. We developed a simple model based on hydrothermal time to predict seedling emergence of tropic ageratum. Hydrothermal time at 2-cm soil depth was calculated from soil moisture and soil temperature simulated from several micrometeorological and soil physical variables. The model was developed using 5 yr of field emergence data from a continuous corn–cassava production system in southwestern Nigeria. Percentage of cumulative seedling emergence from the 5-yr data set was fitted to cumulative soil hydrothermal time using a Weibull function. The predicted cumulative emergence curve significantly matched observed field emergence (r 2 = 0.83). Model predictions were evaluated with root mean square error (RMSE) using four field emergence data sets from southeastern Nigeria (RMSE ≤ 10.1) and Los Banos, Philippines (RMSE = 8.9). RMSE values ≤ 10 indicated that predictions represented observations well. With such models, extension personnel working on tropical soils, especially in West Africa, may be able to provide additional advice to farmers on the appropriate time for the management of tropic ageratum.

The effect of hydration (priming) treatment on dormancy release in annual ryegrass seeds from two populations was investigated. Hydration duration, number, and timing with respect to after-ripening were compared in an experiment involving 15 treatment regimens for 12 wk. Seeds were hydrated at 100% relative humidity for 0, 2, or 10 d at Weeks 1, 6, or 12 of after-ripening. Dormancy status was assessed after each hydration treatment by measuring seed germination at 12-hourly alternating 25/15 C (light/dark) periods using seeds directly from the hydration treatment and seeds subjected to 4 d postpriming desiccation. Seeds exposed to one or more hydration events during the 12 wk were less dormant than seeds that remained dry throughout after-ripening. The longer hydration of 10 d promoted greater dormancy loss than either a 2-d hydration or no hydration. For the seed lot that was most dormant at the start of the experiment, two or three rather than one hydration event or a hydration event earlier rather than later during after-ripening promoted greater dormancy release. These effects were not significant for the less-dormant seed lot. For both seed lots, the effect of a single hydration for 2 d at Week 1 or 6 of after-ripening was not manifested until the test at Week 12 of the experiment, suggesting that the hydration events alter the rate of dormancy release during subsequent after-ripening. A hydrothermal priming time model, usually used for modeling the effect of priming on germination rate of nondormant seeds, was successfully applied to dormancy release resulting from the hydration treatments.

Studies were conducted to develop a model from field and laboratory studies to predict the emergence phenology of Chenopodium album. A mechanistic model to predict the phenology of weed seedling emergence across locations, years, and tillage systems is presented. This was accomplished by the integration of hydrothermal time to describe germination and thermal time to describe shoot elongation. The interaction of soil moisture and temperature in the model was accounted for by the integration of hydrothermal time in algorithms predicting seed germination. Soil temperatures within the weed seed germination zone were predicted by temperature ranges at different depths in the soil. Emergence phenology of C. album seedlings was predicted with greater accuracy under no-till and moldboard plow systems than under a chisel plow system. We attributed this lower accuracy in the chisel plow system to increased heterogeneity in the soil matrix and vertical distribution of the seedbank caused by the chisel plow. The presence or absence of Zea mays did not affect model performance. The use of soil temperature to calculate thermal time was a better predictor of C. album seedling emergence than air temperature. The ability to predict weed seedling emergence phenology is an important component of an integrated weed management strategy.

The objective of this research was to develop a model to predict common cocklebur seedling emergence in spring tillage and no-spring-tillage systems in the presence and absence of a soybean canopy. A Weibull function was used to accumulate heat units (i.e., growing degree days) at a 2.5 cm soil depth on days when mean soil temperature, soil water potential, and soil thermal fluctuation were above established thresholds. The base temperature, soil water potential, and soil thermal fluctuation thresholds used for model development were 17 C, −100 kPa, and 7.5 C, respectively. A single function adequately described common cocklebur seedling emergence in the presence and absence of drill-seeded soybean from data combined over an artificial (2004) and natural seedbank (2005) (R2 = 0.986). Model parameterization differed between the artificial and natural seedbank in the absence of spring tillage, but emergence was adequately described, regardless of soybean presence. Separate parameter estimates for the artificial and natural seedbanks were needed to adequately describe emergence in the system without spring tillage (R2 = 0.975 to 0.984). The ability of the model to account for reduced emergence when soil moisture is limited or when daily thermal fluctuation requirements are not met could assist practitioners with assessments associated with field scouting for weeds as well as other management decisions.

Cogongrass is a noxious perennial grass that has invaded many countries in the tropical and subtropical regions of the world. Its management has been a significant challenge because of large rhizome and bud reserves in the soil. The emergence pattern of this weed under field conditions has received little attention. Field trials were conducted in 2002 and 2003 in the humid forest zone of southeastern Nigeria to model shoot emergence. The experiment had four treatments: (1) count and tag crop-free cogongrass shoots, (2) count and suppress crop-free cogongrass shoots with paraquat, (3) count and cut crop-free cogongrass shoots, and (4) count and cut cogongrass shoots in cultivated corn. The rationale for these treatments was to determine the effect of different monitoring techniques on shoot emergence of cogongrass. The development of the model was based on hydrothermal time, which was calculated from soil moisture and soil temperature at a 2-cm depth. A Weibull function was fitted to cumulative percent shoot emergence values of Treatment 4 and hydrothermal time. The model closely fit the observed pattern of cogongrass shoot emergence (r 2 = 0.95, n = 36). It also predicted shoot emergence satisfactorily in six treatments (r 2 > 0.85, P < 0.001, n = 7 in each treatment) that simulated farmers' practices in southwestern Nigeria. This is the first model developed for cogongrass shoot emergence based on hydrothermal time under field observations. The model should facilitate further analyses of cogongrass emergence patterns and the timing of its management.

Decision-making processes must indicate if, how, and when weed control should be practiced. So far, Decision Support Systems (DSSs) for weed control to prevent crop yield losses can guide decisions on “if” and “how.” Experience shows that farmers need a DSS that can also guide when to treat, but this can only be done if the actual weed density observed in the field is known during the crop cycle. Emergence models allow the prediction of daily density, but precision depends on the survey date. This study focuses on the estimation of the date of the survey for the best prediction of the daily density throughout the crop cycle. The predicted daily density of each species can be used by DSSs without any further survey, saving time and money and improving the use of the DSSs. Results showed that the best date is when the actual density of each weed reaches or exceeds 50% emergence, and this is earlier than the critical point date, supporting the validity of the date estimation method. The possibility to provide specific advice for farmers considering a proper mortality rate of weed seedlings is then discussed. The ability to optimize the date of sampling can improve the reliability of decision-making tools for integrated weed management, in agreement with the European Union goal of sustainable use of pesticides and more environmentally sustainable cropping systems through the use of integrated pest management.

Wild oat is an economically important annual weed throughout small grain producing regions of the United States and Canada. Timely and more accurate control of wild oat may be developed if there is a better understanding of its emergence patterns. The objectives of this research were to evaluate the emergence pattern of wild oat and determine if emergence could be predicted using soil growing degree days (GDD) and/or hydrothermal time (HTT). Research plots were established at Crookston, MN, and Fargo, ND, in 2002 and 2003. On a weekly basis, naturally emerging seedlings were counted and removed from six 0.37-m2 permanent quadrats randomly distributed in a wild oat–infested area. This process was repeated until no additional emergence was observed. Wild oat emergence began between May 1 and May 15 at both locations and in both years and continued for 4 to 6 wk. Base soil temperature and soil water potential associated with wild oat emergence were determined to be 1 C and −0.6 MPa, respectively. Seedling emergence was correlated with GDD and HTT but not calendar days (P = 0.15). A Weibull function was fitted to cumulative wild oat emergence and GDD and HTT. The models for GDD (n = 22, r2 = 0.93, root mean square error [RMSE] = 10.7) and HTT (n = 22, r2 = 0.92, RMSE = 11.2) closely fit observed emergence patterns. The latter model is the first to use HTT to predict wild oat emergence under field conditions. Both models can aid in the future study of wild oat emergence and assist growers and agricultural professionals with planning timely and more accurate wild oat control.

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.

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.

A model that describes the emergence of ripgut brome was developed using a two-season data set from a no-tilled field in northeastern Spain. The relationship between cumulative emergence and hydrothermal time (HTT) was described by a sigmoid growth function (Chapman). HTT was calculated with a set of water potentials and temperatures, iteratively used, to determine the base water potential and base temperature. Emergence of ripgut brome was well described with a Chapman function. The newly-developed function was validated with four sets of data, two of them belonging to a third season in the same field and the other two coming from independent data from Southern Spain. The model also successfully described the emergence in different field management and tillage systems. This model may be useful for predicting ripgut brome emergence in winter cereal fields of semiarid Mediterranean regions.

Robust predictions of weed seedling emergence from the soil seedbank are needed to aid weed management. A common seed accession (Illinois) of giant ragweed was buried in replicate experimental gardens over 18 site years in Illinois, Michigan, Kansas, Nebraska, Ohio, and South Dakota to examine the importance of site and climate variability by year on seedling emergence. In a nonlinear mixed-effects modeling approach, we used a flexible sigmoidal function (Weibull) to model giant ragweed cumulative seedling emergence in relation to hydrothermal time accumulated in each site-year. An iterative search method across a range of base temperature (Tb ) and base and ceiling soil matric potentials (ψb and ψc) for accumulation of hydrothermal time identified optima (Tb = 4.4 C, ψb = −2,500 kPa, ψc = 0 kPa) that resulted in a parsimonious regional model. Deviations between the fits for individual site-years and the fixed effects regional model were characterized by a negative relationship between random effects for the shape parameter lrc (natural log of the rate constant, indicating the speed at which emergence progressed) and thermal time (base 10 C) during the seed burial period October through March (r = −0.51, P = 0.03). One possible implication of this result is that cold winter temperatures are required to break dormancy in giant ragweed seeds. By taking advantage of advances in statistical computing approaches, development of robust regional models now is possible for explaining arable weed seedling emergence progress across wide regions.