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

Features of germination, emergence modeling, longevity, and persistence in Bidens pilosa seed bank | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Features of germination, emergence modeling, longevity, and persistence in Bidens pilosa seed bank Renan Ricardo Zandoná, Francisco de Assis Pujol Goulart, Simone Puntel, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3959684/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To develop models capable of predicting the emergence of hairy beggarticks and assist integrated management, it is fundamental to have knowledge of the environmental factors that influence the germination of the species. The objective of this study was to estimate temperature and water potential cardinals for hairy beggarticks germination, the longevity of its seed bank, and to develop a model of its emergence in the field. Experiments were carried out in the laboratory to determine the temperature and water potential base for seed germination. Eight different temperatures (10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, and 45.0°C), and 10 different water potentials (0, -0.05, -0.1, -0.2, -0.4, -0.6, -0.9, -1.2, -1.5, and − 2.0 MPa) were tested. The field experiments were conducted between 2014 and 2019 using three monitoring seedling emergences. To evaluate the longevity and persistence of the seed bank, a factorial experiment was conducted with three burial depths (0, 3, and 6 cm) and five seed retrieval moments (0, 1, 4, 10, and 16 months). Base, optimal, and maximum temperatures for hairy beggarticks germination are 10.4°C, 24.7°C, and 41.8°C, respectively. The base water potential for the emergence of hairy beggarticks is -0.85 MPa. The thermal and hydrothermal time models are adequate to predict the emergence of hairy beggarticks in different soybean sowing dates. The species has a transient seed bank, however, the greater the seed burial depth, the greater the longevity of the soil seed bank. Biological parameters hydrothermal population dynamics temperature water potential Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The hairy beggartick ( Bidens pilosa L.) is an herbaceous, annual, and sometimes biannual dicotyledonous species with a C3 photosynthetic cycle, being only sexually propagated and belonging to the Asteraceae family (Rojas-Sandoval 2022 ). It originates from South America and tropical environments but is currently found in different regions of the world, becoming a problematic weed in different crops (Stohlgren et al. 2013 ). Currently, four cases of herbicide resistance in the world have been recorded, with two resistant cases reported in Brazil, which have complicated its management. The first herbicide resistance documentation was in 1993 for acetolactate synthase inhibitors (ALS), and the second in 2016 with multiple resistance for ALS and photosystem II inhibitors (PSII), (Heap 2022 ). An important characteristic of hairy beggarticks is its achene-type fruits, which have 2 to 3 awns (Santos and Cury 2011 ), which favors for its epizoochorous dispersal and field establishment from seeds. The plants can flower more than once during the growing season, which has significant implications with respect to seedbank development (Gurvich et al. 2004 ). A single hairy beggarticks plant can produce approximately 30 thousand seeds (Rojas-Sandoval 2022 ). Seeds have greater germination with day and night temperature oscillation close to 25/15°C and 30/20°C, while germination is reduced in dark conditions (Chauhan et al. 2019 ), as occurs with emergence when seeds are buried deeper than 2 cm (Souza et al. 2009 ). The dormancy of hairy beggartick seeds was observed for achenes with warty tegument, which also showed sensitivity to light (Amaral and Takai 1998). The environmental conditions that influence the success of hairy beggartick germination, such as temperature and soil moisture, are critical for field establishment. Therefore, methods for predicting weed emergence based on these conditions can help in proposing management practices that maximize control efficacy (Travlos et al. 2020 ). The identification of base temperatures and water potential for seed germination is necessary to develop models capable of predicting emergence at different times of the year (Werle et al. 2014 ; Royo-Esnal et al. 2015 ). Empirical models have been used to predict weed emergence based on thermal time (Izquierdo et al. 2013 ; Werle et al. 2014 ) or hydrothermal time (García et al. 2013 ; Masin et al. 2014 ; Royo-Esnal et al. 2015 ). These models were developed based on environmental conditions, which allows their use to predict weed emergence in different years and geographic regions (Werle et al. 2014 ). The knowledge about the germination aspects of plants at different times of the year allows for adapting management practices (Zandoná et al. 2018a ), enabling the effective use of herbicides and integrated weed management practices. To obtain effectiveness in integrated weed management, practices focused on weed seeds and aspects of the seed bank should be considered; i.e., crop rotations, soil tillage, and harvest weed seed control (Haring and Flessner 2018 ). Some of these practices aim to limit weed seed rain and dispersal, as well as increase seed soil loss. At the same time, knowledge of the viability and longevity of the weed seed bank in the soil allows determining the survival capacity and persistence of species under different conditions, contributing to decision-making and management (Schwartz-Lazaro and Copes 2019 ). The objective of this study was to estimate temperature and water potential cardinals for hairy beggarticks germination, the longevity of its seed bank, and to develop the model of its emergence in the field. Results Temperature and water potential No seed germination occurred at the temperatures of 10, 35, 40, and 45°C in the first experiment (Fig. 1 A) and 40 and 45°C in the second experiment (Fig. 1 B). In the second experiment, the temperatures of 10, 15, and 35°C had higher germination than in the first, but in both experiments, germination at these temperatures was lower than at the other temperatures. For the other temperatures, cumulative germination of the hairy beggarticks seeds data fit into the four-parameter Weibull sigmoidal model (Fig. 1 ; Table 1 ). The R 2 values ​​ranged from 0.97 to 0.99 and the RMSEP ranged from 3.7 to 8.5, indicating a satisfactory fit of the data into the model. The percentage of germination increased from the temperature of 20°C, and delayed germination was observed in germination at mild temperatures (10–20°C). Table 1 Estimated parameters (a T50, b, c) of the Weibull function adjusted to data of constant temperatures (T) of 10, 15, 20, 25, 30, 35, 40, and 45°C for seed germination of hairy beggarticks. First experiment T a T50 b c R 2 15°C 13.50 ± 0.43 1 11.10 ± 0.35 139464 ns 47368 ns 0.97 20°C 77.64 ± 0.63 3.31 ± 0.07 1.89 ± 0.14 1.19 ± 0.13 0.99 25°C 97.23 ± 0.54 1.83 ± 0.06 2.38 ± 0.28 1.55 ± 0.21 0.99 30°C 92.96 ± 0.63 2.79 ± 0.06 1.30 ± 0.16 1.37 ± 0.25 0.99 Second experiment 10°C 11.23 ± 0.83 15.14 ± 0.27 8.01 ± 2.68 3.02 ± 1.49 0.99 15°C 49.46 ± 0.78 7.98 ± 0.14 6.63 ± 1.18 2.22 ± 0.50 0.99 20°C 77.64 ± 0.63 3.31 ± 0.07 1.89 ± 0.14 1.19 ± 0.13 0.99 25°C 72.90 ± 0.41 4.43 ± 0.07 17.70 ± 5.88 17.77 ± 6.84 0.99 30°C 61.62 ± 0.63 3.50 ± 0.10 3.28 ± 1.03 2.41 ± 0.89 0.98 35°C 22.65 ± 0.25 11.04 ± 0.14 174558 ns 113128 ns 0.99 NS: non-significant parameter. 1 Values ​​represent the standard errors of the mean. The temperatures that are not in the table did not show germination and, therefore, there was no regression adjustment. Based on the estimated T50 values (Table 1 ), the cardinal temperatures for the germination of hairy beggarticks were determined. The parameters of the linear equations adjusted and obtained in the suboptimal and supraoptimal range allowed estimating the base temperature of 10.41°C ± 0.03, optimal temperature of 24.70°C ± 0.04, and maximum of 41.80°C ± 0.05 (Fig. 2 A). In the water potential experiment, it was observed that the maximum percentage of germination decreased with the reduction of water potential (Fig. 1 C, Table 2 ). The cumulative germination curves were fitted to the Weibull function, with R 2 of 0.98 and 0.99 and RMSEP ranging from 3.3 to 10.8, indicating a satisfactory fit of the data into the model. A higher percentage of hairy beggarticks germination occurred with better availability of water for the seeds, whose water potential range between 0 and − 0.2 MPa formed a group of seeds with faster germination – around 80% of them germinated (Fig. 1 C). Decreasing water potential levels in solutions from − 0.6 MPa resulted in germination below 20%, with no seed germination at lower water potentials. The estimate of Ѱb for hairy beggarticks through 1/T 50 was − 0.85 ± 0.05 MPa (Fig. 2 B). Table 2 Estimated parameters (a, T50, b, c) of the Weibull function fitted to water potential data of 0, -0.05, -0.1, -0.2, -0.4, -0.6, -0.9, -1.2, -1.5 and − 2.0 MPa for hairy beggarticks seed germination. Potencial a T50 b c R 2 0.00 MPa 81.7 ± 0.591 1 2.33 ± 0.06 3.12 ± 0.32 1.78 ± 0.22 0.99 -0.05 MPa 70.3 ± 0.42 1.65 ± 0.05 2.21 ± 0.22 1.66 ± 0.19 0.99 -0.1 MPa 75.6 ± 0.55 2.28 ± 0.06 3.04 ± 0.54 2.32 ± 0.49 0.99 -0.2 MPa 74.5 ± 0.88 2.09 ± 0.06 0.23 ± 0.11 0.47 ± 0.14 0.99 -0.4 MPa 65.0 ± 1.70 3.91 ± 0.15 2.83 ± 0.35 1.23 ± 0.23 0.99 -0.6 MPa 24.1 ± 10.3 8.76 ± 7.56 8.73 ± 6.32 1.04 ± 0.74 0.98 1 Values ​​represent the standard errors of the mean. The water potential that is not in the table did not show germination and, therefore, there was no regression adjustment. Mathematical modeling of the emergence The climatic data differed between the three seasons in terms of precipitation, detailing that the average daily temperatures were higher in the later evaluation seasons (Fig. 3 A and B). The precipitation observed during the five years showed that the driest month is November (second simulated soybean sowing date), with rainfall below 50 mm and irregular. For the first year, a greater accumulation of precipitation is seen before October 20th, combined with smaller precipitations that maintain the soil water potential suitable for emergence during the first 10 days of monitoring (Fig. 3 C). These smaller precipitations are also observed in the third simulated sowing date during the five years of monitoring, but since low amounts of precipitation occur in November, the soil water potential is lower or lower than necessary for emergence (Fig. 3 C). As the accumulated volumes of water in the soil were low and the precipitation poorly distributed, different water potentials were observed between the years (Fig. 3 C). By considering the emergence of the hairy beggarticks, associated with environmental variability, it was possible to develop a model of emergence for the species based on thermal time (TT) and hydrothermal time (TH). Both models described emergence at the three monitoring times using the sigmoidal Weibull function with four parameters (Fig. 4 ). The similarity of the predictive response of the models is due to the large number of data and the variability observed in different years. In addition, in a few moments, the water potential of the soil was below the Ѱb of the species. It was observed that even though the TT model had a satisfactory fit, some parameters were not significant for hairy beggarticks in November (the second simulated sowing date), in addition to the lowest R 2 value observed (Model C), (Table 3 ). The accuracy of the TH model is greater and adjusts at all times as a function of the water potential also influencing the accumulation of soil temperature, and not just the Tb, as both models started accumulating temperature at the time of installation of the experiment. This is confirmed by the higher R 2 and lower standard errors obtained in the TH models (Table 3 ). Table 3 Estimated parameters (a, T50, b, c) of the Weibull function fitted to the thermal and hydrothermal time model for hairy beggarticks seed germination. Model a T50 b c R 2 A 93.94 ± 2.87 1 107.44 ± 5.83 355.27 ± 29.99 4.54 ± 0.59 0.85* B 96.05 ± 5.02 137.61 ± 8.29 101.01 ± 16.92 1.08 ± 0.27 0.87* C 95.91 ± 5.28 191.08 ± 15.66 112551.66 ns 16091.86 ns 0.63* D 88.53 ± 2.56 16.25 ± 1.24 15.88 ± 4.04 1.06 ± 0.49 0.74* E 90.27 ± 12.29 162.70 ± 19.72 133.02 ± 31.61 1.01 ± 0.61 0.70* F 78.17 ± 4.57 17.89 ± 2.27 17.37 ± 4.12 0.73 ± 0.20 0.77* * The model is significant. NS: non-significant parameter. 1 Standard error. A and B in the first season; C and D in the second season; E and F in the third season, for TT and TH respectively. The emergence of hairy beggarticks, in the three simulated sowing dates, is greater in October (first sowing date), (Fig. 4 ). However, it is possible to highlight the predominance of the greatest emergence of hairy beggarticks in the third simulated sowing date, observed in the years 2014, 2017, and 2018 (Fig. 4 E and F). Until the first half of November (second sowing date), there was predominantly a continuous emergence of hairy beggarticks, while in December (third sowing date), there was a higher speed of emergence. This result can be better observed through the dispersion of points of accumulated emergence observed in the third simulated sowing date (Fig. 4 E and F), as well as by the smaller values of the coefficients a and c of the models (Table 3 ). The highest density of hairy beggarticks plants occurred in the second simulated soybean sowing date, with an average of 41 plants m − 2 (Fig. 4 C and D). Longevity and persistence of seed bank Data analysis showed a significant interaction between burial depth and retrieval moments only for the remaining seeds variable. Meanwhile, in the other variables, germination, dormancy, mortality, and persistence, the simple effect of retrieval moments was verified (Fig. 5 ). For the remaining seeds variable, there was a reduction in the percentage of seeds rescued over the course of the retrieval moments, regardless of the burial depth, where the data was adjusted to the decreasing exponential regression equation (Fig. 5 A). The number of retrieved seeds decreased considerably after 10 months of burial, and in the last evaluation at 16 months, only 3.2, 5.5 and 9% were found for burial depths 0, 3, and 6 cm, respectively, not differing from each other given the overlapping of the confidence intervals of the means. The hairy beggarticks seeds showed germination close to 80% at retrieval moment 0 (Fig. 5 B). Thus, at the time of burial, only 8.3% of the seeds were dormant (Fig. 5 C). However, it was observed that approximately 43% of the seeds collected at 2 and 4 months after burial germinated when submitted to laboratory testing, approaching zero in subsequent evaluations (Fig. 5 B). Such results are justified in view of the mortality of the remaining seeds, which showed a linear and increasing behavior during the evaluated time (Fig. 5 D), reaching approximately 10% after 16 months of evaluation. Thus, almost all seeds did not show viability by the tetrazolium test, giving persistence close to zero after 16 months, for all burial depths (Fig. 5 E), suggesting that the species has a transient seed bank in the soil. The seed bank outputs were high based on remaining seeds and mortality data (Fig. 5 A and D), predisposing the species to the need for annual restitution of the seed bank, since, after 16 months from burial, an average of 5% of seeds were recovered and presented a low level of germination (Figs. 5 A and B). Exits from the hairy beggarticks seed bank can be explained predominantly by the high level of predation and seed decay at the different burial depths evaluated, approaching 100% at a depth of 0 cm (Fig. 6 A) and about 90% at 6 cm (Fig. 6 C). The dormancy was not a relevant factor for the maintenance of hairy beggarticks seeds in the soil, since it was observed that a maximum of 5% of buried seeds were dormant between 2 and 4 months after burial (Fig. 6 ). On the other hand, the mortality of the seeds remained relatively constant, representing the seeds recovered without viability (Fig. 6 ). Discussion Temperature and water potential Similar results were observed in populations of hairy beggarticks from different regions of Brazil, most of which samples germinated at 10 to 35°C (Barros et al. 2017 ). Temperature values for maximum germination of hairy beggarticks for an Australian population were estimated with temperatures alternating between 25/15°C or 30/10°C (Chauhan et al. 2019 ), or 15°C for populations from different geographic origins (Barros et al. 2017 ). Regarding the water potential data, the complete inhibition of germination of hairy beggarticks seeds was observed at water potentials below − 0.8 MPa through saline solution (Chauhan et al. 2019 ), attributing to the enzymatic inhibition necessary for the germination process (Marcos Filho 2015 ). There may be differences when comparing water potentials using saline or PEG solution since NaCl can cause seed toxicity. However, corroborating these results, it was possible to estimate Ѱb for hairy beggarticks. Mathematical modeling of the emergence Precipitation associated with the increase in average daily temperature stimulates new emergence of weeds, so this variability in precipitation over the years, with changes in soil water potential, is highly desirable for the development of models based on the region's microclimate (Royo-Esnal et al. 2015 ). The models showed very similar predictability for each season, but the model based on the TT predicts emergence in a staggered way, while the TH predicts emergence faster and in a shorter period. Therefore, TH models improve the accuracy of TT model predictions for weed emergence, particularly in locations where periods of water deficit occur (Leguizamón et al. 2005 ). However, the influence of TH may occur for Helianthus annuus L. even under normal soil water availability conditions (Werle et al. 2014 ). The TH model was also more accurate than the TT model for the emergence of Conyza bonariensis L. (Zambrano-Navea et al. 2013 ). The highest emergence at the beginning of the crop cycle (first sowing date) are a tendency reported for several weed species, including both poaceae and eudicots (Zandoná et al. 2018b ). This behavior generally ensures success in the establishment and perpetuation of the species. However, it is difficult to determine exactly the causes of the reduction or increase in the number of weed emergence in each emergence, as several factors may be involved. Among the main aspects, the climatic changes during the seasons and years stand out (Fig. 3 ), the non-revolving of the soil, the soil cover (oat straw), and the dormancy conditions, which certainly may have interfered with the emergence and establishment of weeds. Furthermore, the density of the species in the area is important information for determining the competitive capacity (Balbinot Jr et al. 2003 ), moreover, it is noteworthy that even at low densities, such as 8 plants per m -2 , hairy beggarticks can promote up to a 10% reduction in soybean productivity (Rizzardi et al. 2003 ). The emergence model for hairy beggarticks seems to be robust enough to be used as a tool in weed management, due to the large data set used. The larger the dataset, the better the parameter estimation. However, models with large and complex data input sets are not always complete and reliable (Colbach et al. 2006 ). The benefit of using TH instead of TT is due to its ability to predict possible emergence pauses caused by low soil moisture, which is important to reduce error in practice; i.e., when the emergence model is being applied (Masin et al. 2014 ). Thus, for the first season, the recommended chemical control would be carried out with the application of a post-emergence herbicide with a residual effect, due to the slower emergence. For the November (second) and December (third) simulated sowing dates, it is necessary to carry out burndown associated with a pre-emergent herbicide to guarantee a residual effect and the initial establishment of the crop free from the presence of weeds. It should also be noted that the models can be used as parameters for planning management practices for several summer crops cultivated in the no-tillage system. Longevity and persistence of seed bank The main source of supply for future weed infestations comes from the seed bank, which presents a dynamic behavior, with inputs coming from immigration or from the seeds produced and dispersed in the area. On the other hand, the outputs occur due to germination, aging, loss of viability, predation, and decay (Chauhan and Johnson 2010 ). In the case of the hairy beggarticks under study, seed bank outputs were high based on remaining seeds and mortality data (Fig. 5 A and D). Exits from the hairy beggarticks seed bank can be explained predominantly by the high level of predation and seed decay at the different burial depths evaluated. Similar results were found for ryegrass ( Lolium multiflorum ) in which the number of dormant seeds reduced to less than 20% after 60 days of burial (Cechin et al. 2021 ). However, for horseweed ( Conyza sp.), the maximum percentages of dormancy were close to 3 months (Vargas et al. 2016). Due to the results observed for this species, it is possible to conclude that management practices that limit the germination and emergence of the species – if properly conducted – are effective due to the reduced persistence of the species in the seed bank in the soil. Thus, the results of the base, optimum, and maximum temperatures for hairy beggarticks emergence are 10.4, 24.7, and 41.9°C, respectively, and the base water potential is -0.85 MPa. Both thermal time and hydrothermal models are suitable for predicting hairy beggarticks emergence. Therefore, they can be used as a tool for decision-making regarding the use of control measures based on environmental conditions. The species exhibits a transient seed bank, with high losses due to predation and decay. Based on the results obtained for this species, it is possible to elaborate management strategies based on climatic conditions that favor the emergence of hairy beggarticks in different sowing times of crops, or that provide the correct positioning of pre- and post-emergence herbicides. In addition, understanding the dynamics of hairy beggarticks seeds in the soil allows the adoption of management strategies that help in the depletion or renewal of the seed bank. Material and methods Experimental conditions and location The cardinal temperatures and water potential experiments were carried out in the Seed Analysis laboratory of the Faculdade de Agronomia Eliseu Maciel (FAEM/UFPel), in growth chambers (model EL 222, manufacturer ELETROlab®) with a photoperiod of 8/16 hours of light/dark. The growth chamber was composed of six fluorescent lamps with 40 watts. The remaining experiments (modeling the emergence, longevity, and persistence of the hairy beggarticks seed bank) were conducted in field conditions between the years 2014 and 2019. The experimental area was the Centro Agropecuário da Palma (CAP), belonging to the Centro de Estudos em Herbologia (CEHERB), Capão do Leão – Rio Grande do Sul (RS), Brazil (31.80°S; 52.50°W). The soil in the area is classified as Ultisol, with a sandy loam texture, belonging to the Pelotas mapping unit (Santos et al. 2018 ). Validation of the emergence data was carried out at the Experimental station of Cooperativa Central Gaúcha Ltda (CCGL), in Cruz Alta – RS, Brazil (28.60°S; 53.67°W), which has soil classified as Oxisol (Santos 2018). The regions have a humid subtropical climate (class Cfa), being humid temperate, with hot summers and no defined dry season. Seed collection and origin For temperature and water potential experiments, and longevity of the seed bank the hairy beggarticks seeds were previously collected from different plants with mature terminal capitula from March to April 2016. The seeds were collected from different hairy beggarticks populations, located at CAP, CCGL, and the county of Barra Funda (27.93ºS; 59.04ºW). The seeds from different locations were mixed for use the in experiments, and subsequently evaluated for seed quality (seed germination, viability, and dormancy). The viability of the seeds was evaluated through the tetrazolium test (Mapa 2009 ). For experiments of modeling emergence, the seedlings evaluated originated from the seed bank, without previous hairy beggarticks sowing. Cardinal temperatures and base water potential Three experiments were conducted in a completely randomized design (CRD), with four replications of 50 seeds each, two to determine cardinal temperature and one for water potential. In the first and second experiments, the seeds were exposed to eight constant temperatures, 10, 15, 20, 25, 30, 35, 40, and 45°C. Based on the results, the third experiment to determine the base water potential was conducted using the temperature of 24.7°C. For the experiment, 10 water potentials were tested as treatments: 0, -0.05, -0.1, -0.2, -0.4, -0.6, -0.9, -1.2, -1.5, and − 2.0 MPa. For the simulation of water potentials, polyethylene glycol 8000 (PEG) was used in different concentrations to form the solutions (Hardegree and Emmerich 1994 ). The seeds were sown on blotting paper, and previously moistened with distilled water (temperature experiment) or PEG solution (water potential experiment). The proportion for water or PEG was 2.5 times the mass of the dry paper placed in acrylic boxes with dimensions of 11 x 11 x 3.5 cm (Mapa 2009 ). The germinated seeds were counted and removed daily to better estimate accumulated germination, being considered germinated when the radicle length reached up to 2 mm. Tests were considered complete when no further germination occurred for five days. Emergence modeling Field experiments were conducted for 5 years (2014 to 2018). For each year the simulated sowing dates were October 20 (first), November 10 (second), and December 1 (third). Prior to trial establishment, the experimental area was planted to oats at 80kg ha − 1 of seed density, with no soil disturbance. Burndown applications with glyphosate (1440 g a.e. ha − 1 ) and paraquat (300 g a.i. ha − 1 ) were made 15 and zero days, respectively, before the start of each simulated soybean sowing dates to avoid counting hairy beggartick plants before the start of emergence monitoring. The seed bank survey from zero to 5 cm of depth of the area showed 14 different species from 10 distinct botanical families amounting to 73,543 seeds m − 2 (data not shown). By analyzing the composition of the Bidens pilosa seed bank during the trials, an average of 1,416.38 seeds m − 2 was observed in the first 5 cm of depth. Emergence monitoring of hairy beggarticks from the soil seed bank was carried out every four days for each simulated sowing date up to 24 days, and an additional count at 48 days after October 20, November 10, and December 1 for the different years. The additional count coincided with the total interference prevention period for the soybean crop (Zandoná et al. 2018b ). The one-hectare (ha) area was divided into four blocks (four repetitions), which received four plots of 15.75 m 2 (3.15 x 5 m) per season. Each plot was monitored with four replicates, and all weeds that emerged in a useful area of 0.25 m 2 were counted. Plants were considered to have emerged when at least one centimeter of their aerial part reached above the ground. 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 five-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. The transformation of moisture percentage data into soil water potential followed the average soil water retention equation proposed by Bortoluzzi et al. ( 2008 ) for the no-tillage system. Longevity and persistence of the seed bank The experiment started in July 2017 and finished in November 2018. The 125 seeds were distributed in 50g of soil of the Ultisols type and packed in nylon mesh bags (10 x 10 cm), which constituted the experimental units. This number of seeds was selected to obtain 50 viable seeds in the sample. The treatments were arranged in a factorial scheme (3 x 5) in a randomized block design, with four replications. Factor A corresponded to three seed burial depths (0, 3, and 6 cm) and factor B comprised five collection/rescue times (0, 1, 4, 10, and 16 months after the bags were buried), (Vargas et al. 2018 ). It is noteworthy that for the burial depth of 0 cm, the experimental units were arranged on the soil surface. In the area where the experiment was installed, soybeans were cultivated during the summer, while in the winter the area was kept fallow. Each season, the collection was carried out by removing the seeds from the bag. Then, they were cleaned by washing the sample with water in a set of sieves of sizes 16, 32, and 60 mesh (Vargas et al. 2018 ). After washing, the sample was left on filter paper to dry for 24 hours and then analyzed in a light stereoscope to extract the remaining seeds (Vargas et al. 2018 ). These were submitted to the germination test to determine the physiological quality, as described in Mapa ( 2009 ). Seeds that did not germinate were submitted to the tetrazolium test (Mapa 2009 ) to identify viability. Seeds that did not germinate and were viable were considered dormant, while non-viable seeds after the tetrazolium test were considered dead. Unrecovered seeds were considered predated, decayed, or any other cause of loss, including seed germination. The variables analyzed in this experiment were the remaining seeds, germination, dead seeds, and dormant seeds, with the results expressed in percentage (Mapa 2009 ). The percentage of remaining seeds was calculated based on the initial number of seeds per repetition, while the percentages of germination, dead, and dormant seeds were calculated based on the number of remaining seeds. Also, persistence was calculated, which expresses the sum of the number of seeds germinated in the laboratory and viable seeds in the tetrazolium test, deducted from the original number of seeds per repetition, and expressed as a percentage. Statistical analysis For all experiments, the data obtained were analyzed for normality (Shapiro Wilk test) and homoscedasticity (Hartley test), and subsequently submitted to analysis of variance (p ≤ 0.05). When statistical significance was found, regression analysis was performed using R software scripts (R Core Team 2012). For the temperature and water potential experiments, the cumulative germination rates were determined. For this purpose, the temperature and water potential data were adjusted to the Weibull logistic function (Eq. 1), which allowed for estimating the time required to obtain 50% germination for each treatment (T 50 ). \(\text{y=a [1-} {\text{e}}^{\text{- (x-T50 +} \frac{\text{bln2}\frac{\text{1}}{\text{c}}\text{ }}{\text{b}}\text{)c} }\text{]}\) [1] where: y is the percentage of germination; x is the time expressed in days, TT , or TH ; a is the maximum recorded emergence percentage; b is the rate of increase; c is a shape parameter; and T 50 is the time (days), TT , or TH required to achieve 50% germination or emergence. To determine the base, optimal and maximum temperatures (Tb, To, and Tmax respectively) the germination rate was estimated at 1/T 50 , and two independent linear regressions (sub- and supra-optimal) were generated, according to the methodology proposed by Dumur et. al ( 1990 ). The base water potential (Ѱb) was calculated by plotting 1/T 50 for each water potential and determined by the intersection of the regression line with the abscissa (Scherner et al. 2017 ). Confidence intervals (p ≤ 0.05) were calculated using the bootstrap statistical procedure to obtain estimates of Tb, To, and Tmax, whose criterion is the one with the smallest deviation from the residuals (Loddo et al. 2017 ). These values ​​were used to calculate the thermal time (TT) and hydrothermal time (TH) for each combination of temperature and water potential in the field. For field-condition emergence modeling experiments, the data were converted to cumulative emergence, based on the total emergence of emerged seedlings. Likewise, soil temperature and moisture data were used to determine thermal (TT) and hydrothermal (TH) time, according to the methodology proposed by Gummerson ( 1986 ). Then, the relationship between cumulative emergence was described by the Weibull model (Eq. 1). The developed model was validated with emergence data from the experimental station of the CCGL company, and the actual values ​​of emergence and the values ​​estimated by the model were analyzed by mean squared error (MSE), (Mayer and Butler 1993 ; Roman et al. 2000 ) and by the Akaike Information Criterion (AIC), (Qi and Zhang 2001 ). In the seed bank evaluation experiment, regression analysis was modeled for the retrieval moments. The remaining seed variables, germination, dormancy, and persistence fit into the three-parameter decreasing exponential regression equation (Eq. 2): y = y0 + a*e(-b*x) [2] where: y = response variable of interest; x = collection times; e = exponential function; y0 = is the intercept (response value for x = 0); a = difference between the maximum and minimum points of the variable; and b = slope of the curve. A linear polynomial regression equation was the most appropriate for the data on the mortality variable (Eq. 3): y = a + bx [3] where: y = response variable of interest; x = collection times; a = is the intercept or linear coefficient; and b = represents the slope of the line. The state of the seeds in the soil in time (months) was estimated by the different variables: germination, mortality, viability (viable and non-viable seeds), and predation or deterioration of the seeds, based on the averages. Declarations Acknowledgments We are grateful to National Research Council (CNPq) (Proc. 308363/2018-3) for the research fellowships granted to several of the authors with of undergraduate and graduate scholarships during the course of this work. Conflict of interest The authors declare that they have no conflict of interest. References Amaral A, Takaki M (1998) Achene dimorphism in Bidens pilosa L. as determined by germination test. Braz. Arch. Biol. Technol. http://doi.org/10.1590/S1516-89131998000100002 Balbinot Jr AA, Fleck NG, Barbosa Neto JF, Rizzardi MA (2003) Características de plantas de arroz e a habilidade competitiva com plantas daninhas. Planta Daninha. http://doi.org/10.1590/S0100-83582003000200001 Barros RT, Martins CC, Silva GZ, Martins D (2017) Origin and temperature on the germination of beggartick seeds. Rev Bras Eng Agric Ambient. http://doi.org/10.1590/1807-1929/agriambi.v21n7p448-453 Bortoluzzi ED, Silva VR, Petry C, Cecchetti D (2008) Porosidade e retenção de água em um Argissolo sob manejo convencional e direto submetido a compressões unidimensionais. R Bras Ci Solo. http://doi.org/10.1590/S0100-06832008000400009 Cechin J, Schmitz MF, Hencks JR, Vargas AAM, Agostinetto D, Vargas L (2021) Burial depths favor Italian ryegrass persistence in the soil seed bank. Sci Agric. http://doi.org/10.1590/1678-992X-2019-0078 Chauhan BS, Ali HH, Florentine S (2019) Seed germination ecology of Bidens pilosa and its implications for weed management. Sci Rep. http://doi.org/10.1038/s41598-019-52620-9 Chauhan BS, Johnson DE (2010) The role of seed ecology in improving weed management strategies in the tropics. Adv Agron. http://doi.org/10.1016/S0065-2113(10)05006-6 Colbach N, Dürr C, Roger-Estrade J, Chauvel B (2006) AlomySys: Modelling black-grass ( Alopecurus myosuroides Huds.) germination and emergence, in interaction with seed characteristics, tillage and soil climate: I. Construction. Eur J Agron. http://doi.org/10.1016/j.eja.2005.07.001 Dumur D, Pilbeam CJ, Craigon J (1990) Use of the Weibull function to calculate cardinal temperatures in faba bean. J Exp Bot. http://doi.org/10.1093/jxb/41.11.1423 García AL, Recasens J, Forcella F, Torra J, Royo-Esnal A (2013) Hydrothermal emergence model for ripgut brome ( Bromus diandrus ). Weed Sci. http://doi.org/10.1614/WS-D-12-00023.1 Goulart FAP, Zandoná RR, Schmitz MF, Ulguim AR, Andres A, Agostinetto D (2020) Modeling the emergence of Echinochloa sp. in flooded rice systems. Agron. http://doi.org/10:1-9, 10.3390/agronomy10111756 Gummerson RJ (1986) The effect of constant temperatures and osmotic potential on the germination of sugar beet. J Exp Bot. http://doi.org/10.1093/jxb/37.6.729 Gurvich DE, Enrico L, Funes G, Zak MR (2004) Seed mass, seed production, germination and seedling traits in two phenological types of Bidens pilosa (Asteraceae). Aust J Bot. http://doi.org/10.1071/BT03172 Hardegree SP, Emmerich WE (1994) Seed germination response to polyethylene glycol solution depth. Seed Sci Technol 22:1-7 Haring SC, Flessner ML (2018) Improving soil seed bank management. Pest Manage Sci. http://doi.org/10.1002/ps.5068 Heap I (2022) The International Survey of Herbicide Resistant Weeds. http://weedscience.org. Accessed 13 November 2022 Izquierdo J, Bastida F, Lezaún JM, Sánchez del Arco MJ, Gonzalez‐Andujar JL (2013) Development and evaluation of a model for predicting Lolium rigidum emergence in winter cereal crops in the Mediterranean area. Weed Res. http://doi.org/10.1111/wre.12023 Leguizamón ES, Fernandez‐Quintanilla C, Barroso J, Gonzalez‐Andujar JL (2005) Using thermal and hydrothermal time to model seedling emergence of Avena sterilis ssp. ludoviciana in Spain. Weed Res. http://doi.org/10.1111/j.1365-3180.2004.00444.x Loddo D, Farshid GF, Zahra R, Roberta M (2017) Base temperatures for germination of selected weed species in Iran. Plant Prot Sci. http://doi.org/10.17221/92/2016-PPS Mapa (2009) Regras para análise de sementes. Ministério da Agricultura Pecuária e Abastecimento (MAPA), Brasília Marcos Filho J (2015) Fisiologia de Sementes de Plantas Cultivadas. Abrates, Londrina Masin R, Loddo D, Gasparini V, Otto S, Zanin G (2014) Evaluation of weed emergence model AlertInf for maize in soybean. Weed Sci. http://doi.org/10.1614/WS-D-13-00112.1 Mayer DG, Butler DG (1993) Statistical validation. Ecol Modell. http://doi.org/10.1016/0304-3800(93)90105-2 Qi M, Zhang GP (2001) An investigation of model selection criteria for neural network time series forecasting. Eur J Oper Res. http://doi.org/10.1016/S0377-2217(00)00171-5 R Development Core team (2012) R: a language and environment for statistical computing. R foundation for statistical computing. https://www.r-project.org/ Rizzardi MA, Fleck NG, Mundstock CM, Bianchi MA (2003) Perdas de rendimento de grãos de soja causadas por interferência de picão-preto e guanxuma. Cien Rural. http://doi.org/10.1590/S0103-84782003000400005 Rojas-Sandoval J (2022) Bidens pilosa (blackjack). Invasive Species Compendium CABI. https://doi.org/10.1079/cabicompendium.9148 Roman ES, Murphy SD, Swanton CJ (2000) Simulation of Chenopodium album seedling emergence. Weed Sci. http://doi.org/10.1614/0043-1745(2000)048[0217:SOCASE]2.0.CO;2 Royo-Esnal A, Necajeva J, Torra J, Recasens J, Gesch RW (2015) Emergence of field pennycress ( Thlaspi arvense L.): comparison of two accessions and modelling. Ind. Crops Prod. http://doi.org/10.1016/j.indcrop.2014.12.010 Santos HG et al. (2018) Sistema brasileiro de classificação de solos. 5th ed. Brasília, DF: Embrapa. 356 p Santos JB, Cury JP (2011) Picão-preto: uma planta daninha especial em solos tropicais. Planta Daninha. http://doi.org/10.1590/S0100-83582011000500024 Scherner A, Melander B, Jensen PK, Kudsk P, Avila LA (2017) Germination of winter annual grass weeds under a range of temperatures and water potentials. Weed Sci. http://doi.org/10.1017/wsc.2017.7 Schwartz-Lazaro LM, Copes JT (2019) A review of the soil seedbank from a weed scientists perspective. Agron. http://doi.org/10.3390/agronomy9070369 Souza MC, Pitelli RA, Simi LD, Oliveira MCJ (2009) Emergência de Bidens pilosa em diferentes profundidades de semeadura. Planta Daninha. http://doi.org/10.1590/S0100-83582009000100005 Stohlgren TJ et al. (2013) Globalization effects on common plant species. Encyclopedia of Biodiversity. https://doi.org/10.1016/B978-0-12-384719-5.00239-2 Travlos I, Gazoulis I, Kanatas P, Tsekoura A, Zannopoulos S, Papastylianou P (2020) A key factors affecting weed seeds’ germination, weed emergence, and their possible role for the efficacy of false seedbed technique as weed management practice. Front Agron. http://doi.org/10.3389/fagro.2020.00001 Vargas AAM, Agostinetto D, Zandonár RR, Fraga DS, Avila Neto RC (2018) Longevity of horseweed seed bank depending on the burial depth. Planta Daninha. http://doi.org/1590/S0100-83582018360100050 Werle R, Sandell DL, Buhler DD, Hartzler RG, Lindquist JL (2014) Predicting emergence of 23 summer annual weed species. Weed Sci. http://doi.org/10.1614/WS-D-13-00116.1 Zambrano‐Navea C, Bastida F, Gonzalez‐Andujar JL (2013) A hydrothermal seedling emergence model for Conyza bonariensis . Weed Res. http://doi.org/10.1111/wre.12020 Zandoná RR, Agostinetto D, Ruchel Q (2018a) Modelagem matemática do fluxo de emergência de plantas daninhas: Ferramenta para decisão no manejo de cultivos. Rev Bras Herb. http://doi.org/10.7824/rbh.v17i1.538 Zandoná RR, Agostinetto D, Silva BM, Ruchel Q, Fraga DS (2018b) Interference periods in soybean crop as affected by emergence times of weeds. Planta Daninha. http://doi.org/10.1590/S0100-8358201836010004 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3959684","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273210871,"identity":"cd11fa59-37b4-48ee-bf1e-269970a31a5e","order_by":0,"name":"Renan Ricardo Zandoná","email":"","orcid":"","institution":"Universidade Federal de Pelotas","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Renan","middleName":"Ricardo","lastName":"Zandoná","suffix":""},{"id":273210872,"identity":"f0c335d6-002b-4878-9581-d8994bdb69a5","order_by":1,"name":"Francisco de Assis Pujol Goulart","email":"","orcid":"","institution":"Universidade Federal de Pelotas","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"de Assis Pujol","lastName":"Goulart","suffix":""},{"id":273210873,"identity":"fb1b26e8-3b4b-4027-a4ef-abfd7998e435","order_by":2,"name":"Simone Puntel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACZgST8UBChQ2IbjxAnBY2BoYDD86kgbQ04NcCB0AtBx+2HAaz8WqRb+dOfFxQcSdxu3zzgQOJDeft1rYfBtpSYxONS4vBYd7NxjPOPEvc2caWcCBxx+3kbWcSgVqOpeU24NLCzLtNmrftcOKGYzwGBxLP3E42A9p1gLHhME4t8s0gLf9AWvg/HEhsO5dsdv4hfi0Mh0FaGsC2MAC1HLAzu0HAFrBfeI49M95wLM3gQMKZ5ASzG0BbEvD4Rb7/7MbHPDV3ZDccPvzw4Y8KO3uz8+kPH3yoscHtMAg4AGclglUm4FeOqsWesOJRMApGwSgYaQAA1SRvHq7MdNsAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Santa Maria (UFSM)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Simone","middleName":"","lastName":"Puntel","suffix":""},{"id":273210874,"identity":"550f5851-0e79-4929-8cc9-72be4764ac8d","order_by":3,"name":"André Da Rosa Ulguim","email":"","orcid":"","institution":"Federal University of Santa Maria (UFSM)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"André","middleName":"Da Rosa","lastName":"Ulguim","suffix":""},{"id":273210875,"identity":"d430622d-3bad-4cf7-886c-a828cad374a6","order_by":4,"name":"Dirceu Agostinetto","email":"","orcid":"","institution":"Universidade Federal de Pelotas","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dirceu","middleName":"","lastName":"Agostinetto","suffix":""}],"badges":[],"createdAt":"2024-02-15 20:47:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3959684/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3959684/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51299793,"identity":"30a83d9e-c728-493d-9268-b9d52cb47568","added_by":"auto","created_at":"2024-02-19 06:33:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":390750,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative germination curves for hairy beggarticks at different constant temperatures in the first (A) and second experiment (B), and at different water potentials (C) as a function of time in days. Lines were fitted to the Weibull model for each data series.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3959684/v1/382030a379e75d2a610a2205.png"},{"id":51299794,"identity":"c2d3faec-da86-4c4c-9a45-c0ec2252834d","added_by":"auto","created_at":"2024-02-19 06:33:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":239398,"visible":true,"origin":"","legend":"\u003cp\u003eRegression line fitted to 1/T50 results in the suboptimal and supraoptimal temperature ranges and in response to different temperatures (A), or water potentials (MPa), (B) for hairy beggarticks. Filled and empty symbols represent the observed data from the first and second experiments, respectively, and the lines are the fitted linear regressions.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3959684/v1/f11aa1f3015fedd0a728a9ff.png"},{"id":51299792,"identity":"9706d10e-bdfb-41d4-9948-decda1577a10","added_by":"auto","created_at":"2024-02-19 06:33:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":641901,"visible":true,"origin":"","legend":"\u003cp\u003eClimatic data observed in the experimental area in Capão do Leão (RS) during the experiment: daily average air temperature (A), accumulated precipitation for 15 days (B), and daily soil water potential (MPa) during the years 2014, 2015, 2016, 2017 and 2018.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3959684/v1/167fb3edfd0ea6a7d48c3347.png"},{"id":51299795,"identity":"b4017888-c070-4392-ad56-24e67e25f516","added_by":"auto","created_at":"2024-02-19 06:33:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":447408,"visible":true,"origin":"","legend":"\u003cp\u003eModel of thermal time (A, C, and E) and hydrothermal time (B, D and F) for cumulative emergence (%) of hairy beggarticks for the first (A and B), second (C and D), and third (E and F) emergences in the growing seasons of 2014, 2015, 2016, 2017, and 2018 in Capão do Leão (RS). Dashed lines represent predicted emergence, and symbols represent observed emergence.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3959684/v1/f32258376165c9135381add7.png"},{"id":51299797,"identity":"2c0a8445-d915-4174-8976-81d183fcbc6e","added_by":"auto","created_at":"2024-02-19 06:33:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":599710,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of remaining seeds (A) germination (B), dormancy (C), mortality (D), and persistence (E) of hairy beggarticks after the germination test as a function of burial depth and collection time (months). The dots represent the average values ​​of the repetitions of each depth in each season, and the bars represent the respective 95% confidence intervals. July corresponds to the time of burial in month zero.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3959684/v1/4e6883c3248e57bc6fda6068.png"},{"id":51300774,"identity":"640ae647-e190-42f5-af7a-33a7bc1acc65","added_by":"auto","created_at":"2024-02-19 06:41:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":187299,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in time (months) of the condition of the hairy beggarticks seeds in the soil as a function of the time of collection and depth of collection – 0 cm (A), 3 cm (B), and 6 cm (C) – on germination, mortality, dormancy, and predation or decay. July corresponds to the time of burial in month zero.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3959684/v1/31ee54d4165da9ed13c369f8.png"},{"id":51654056,"identity":"8a1f7ad2-3624-4710-a5f2-ceebd6ac82bc","added_by":"auto","created_at":"2024-02-26 16:23:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1709896,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3959684/v1/3b553764-aeec-473e-b082-d475b819c535.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Features of germination, emergence modeling, longevity, and persistence in Bidens pilosa seed bank","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe hairy beggartick (\u003cem\u003eBidens pilosa\u003c/em\u003e L.) is an herbaceous, annual, and sometimes biannual dicotyledonous species with a C3 photosynthetic cycle, being only sexually propagated and belonging to the Asteraceae family (Rojas-Sandoval \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It originates from South America and tropical environments but is currently found in different regions of the world, becoming a problematic weed in different crops (Stohlgren et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Currently, four cases of herbicide resistance in the world have been recorded, with two resistant cases reported in Brazil, which have complicated its management. The first herbicide resistance documentation was in 1993 for acetolactate synthase inhibitors (ALS), and the second in 2016 with multiple resistance for ALS and photosystem II inhibitors (PSII), (Heap \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn important characteristic of hairy beggarticks is its achene-type fruits, which have 2 to 3 awns (Santos and Cury \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which favors for its epizoochorous dispersal and field establishment from seeds. The plants can flower more than once during the growing season, which has significant implications with respect to seedbank development (Gurvich et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). A single hairy beggarticks plant can produce approximately 30 thousand seeds (Rojas-Sandoval \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Seeds have greater germination with day and night temperature oscillation close to 25/15\u0026deg;C and 30/20\u0026deg;C, while germination is reduced in dark conditions (Chauhan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as occurs with emergence when seeds are buried deeper than 2 cm (Souza et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The dormancy of hairy beggartick seeds was observed for achenes with warty tegument, which also showed sensitivity to light (Amaral and Takai 1998). The environmental conditions that influence the success of hairy beggartick germination, such as temperature and soil moisture, are critical for field establishment. Therefore, methods for predicting weed emergence based on these conditions can help in proposing management practices that maximize control efficacy (Travlos et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe identification of base temperatures and water potential for seed germination is necessary to develop models capable of predicting emergence at different times of the year (Werle et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Royo-Esnal et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Empirical models have been used to predict weed emergence based on thermal time (Izquierdo et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Werle et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) or hydrothermal time (Garc\u0026iacute;a et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Masin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Royo-Esnal et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These models were developed based on environmental conditions, which allows their use to predict weed emergence in different years and geographic regions (Werle et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe knowledge about the germination aspects of plants at different times of the year allows for adapting management practices (Zandon\u0026aacute; et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e), enabling the effective use of herbicides and integrated weed management practices. To obtain effectiveness in integrated weed management, practices focused on weed seeds and aspects of the seed bank should be considered; i.e., crop rotations, soil tillage, and harvest weed seed control (Haring and Flessner \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Some of these practices aim to limit weed seed rain and dispersal, as well as increase seed soil loss. At the same time, knowledge of the viability and longevity of the weed seed bank in the soil allows determining the survival capacity and persistence of species under different conditions, contributing to decision-making and management (Schwartz-Lazaro and Copes \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The objective of this study was to estimate temperature and water potential cardinals for hairy beggarticks germination, the longevity of its seed bank, and to develop the model of its emergence in the field.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTemperature and water potential\u003c/h2\u003e \u003cp\u003eNo seed germination occurred at the temperatures of 10, 35, 40, and 45\u0026deg;C in the first experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and 40 and 45\u0026deg;C in the second experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In the second experiment, the temperatures of 10, 15, and 35\u0026deg;C had higher germination than in the first, but in both experiments, germination at these temperatures was lower than at the other temperatures. For the other temperatures, cumulative germination of the hairy beggarticks seeds data fit into the four-parameter Weibull sigmoidal model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The R\u003csup\u003e2\u003c/sup\u003e values ​​ranged from 0.97 to 0.99 and the RMSEP ranged from 3.7 to 8.5, indicating a satisfactory fit of the data into the model. The percentage of germination increased from the temperature of 20\u0026deg;C, and delayed germination was observed in germination at mild temperatures (10\u0026ndash;20\u0026deg;C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated parameters (a T50, b, c) of the Weibull function adjusted to data of constant temperatures (T) of 10, 15, 20, 25, 30, 35, 40, and 45\u0026deg;C for seed germination of hairy beggarticks.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003eFirst experiment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eT50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.43\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003eSecond experiment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e174558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e113128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNS: non-significant parameter. \u003csup\u003e1\u003c/sup\u003e Values ​​represent the standard errors of the mean. The temperatures that are not in the table did not show germination and, therefore, there was no regression adjustment.\u003c/p\u003e \u003cp\u003eBased on the estimated T50 values (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the cardinal temperatures for the germination of hairy beggarticks were determined. The parameters of the linear equations adjusted and obtained in the suboptimal and supraoptimal range allowed estimating the base temperature of 10.41\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03, optimal temperature of 24.70\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04, and maximum of 41.80\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the water potential experiment, it was observed that the maximum percentage of germination decreased with the reduction of water potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The cumulative germination curves were fitted to the Weibull function, with R\u003csup\u003e2\u003c/sup\u003e of 0.98 and 0.99 and RMSEP ranging from 3.3 to 10.8, indicating a satisfactory fit of the data into the model. A higher percentage of hairy beggarticks germination occurred with better availability of water for the seeds, whose water potential range between 0 and \u0026minus;\u0026thinsp;0.2 MPa formed a group of seeds with faster germination \u0026ndash; around 80% of them germinated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Decreasing water potential levels in solutions from \u0026minus;\u0026thinsp;0.6 MPa resulted in germination below 20%, with no seed germination at lower water potentials. The estimate of Ѱb for hairy beggarticks through 1/T\u003csub\u003e50\u003c/sub\u003e was \u0026minus;\u0026thinsp;0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 MPa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated parameters (a, T50, b, c) of the Weibull function fitted to water potential data of 0, -0.05, -0.1, -0.2, -0.4, -0.6, -0.9, -1.2, -1.5 and \u0026minus;\u0026thinsp;2.0 MPa for hairy beggarticks seed germination.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotencial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eT50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.00 MPa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.591\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-0.05 MPa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-0.1 MPa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-0.2 MPa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-0.4 MPa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-0.6 MPa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;7.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003e Values ​​represent the standard errors of the mean. The water potential that is not in the table did not show germination and, therefore, there was no regression adjustment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMathematical modeling of the emergence\u003c/h2\u003e \u003cp\u003eThe climatic data differed between the three seasons in terms of precipitation, detailing that the average daily temperatures were higher in the later evaluation seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and B). The precipitation observed during the five years showed that the driest month is November (second simulated soybean sowing date), with rainfall below 50 mm and irregular. For the first year, a greater accumulation of precipitation is seen before October 20th, combined with smaller precipitations that maintain the soil water potential suitable for emergence during the first 10 days of monitoring (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). These smaller precipitations are also observed in the third simulated sowing date during the five years of monitoring, but since low amounts of precipitation occur in November, the soil water potential is lower or lower than necessary for emergence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). As the accumulated volumes of water in the soil were low and the precipitation poorly distributed, different water potentials were observed between the years (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy considering the emergence of the hairy beggarticks, associated with environmental variability, it was possible to develop a model of emergence for the species based on thermal time (TT) and hydrothermal time (TH). Both models described emergence at the three monitoring times using the sigmoidal Weibull function with four parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The similarity of the predictive response of the models is due to the large number of data and the variability observed in different years. In addition, in a few moments, the water potential of the soil was below the Ѱb of the species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt was observed that even though the TT model had a satisfactory fit, some parameters were not significant for hairy beggarticks in November (the second simulated sowing date), in addition to the lowest R\u003csup\u003e2\u003c/sup\u003e value observed (Model C), (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The accuracy of the TH model is greater and adjusts at all times as a function of the water potential also influencing the accumulation of soil temperature, and not just the Tb, as both models started accumulating temperature at the time of installation of the experiment. This is confirmed by the higher R\u003csup\u003e2\u003c/sup\u003e and lower standard errors obtained in the TH models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated parameters (a, T50, b, c) of the Weibull function fitted to the thermal and hydrothermal time model for hairy beggarticks seed germination.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eT50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ec\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;2.87\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e355.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;29.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.85*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e137.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;8.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;16.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.87*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e191.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;15.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e112551.66 ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e16091.86 ns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.63*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.74*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;12.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e162.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;19.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;31.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.70*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.77*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* The model is significant. NS: non-significant parameter. \u003csup\u003e1\u003c/sup\u003e Standard error. A and B in the first season; C and D in the second season; E and F in the third season, for TT and TH respectively.\u003c/p\u003e \u003cp\u003eThe emergence of hairy beggarticks, in the three simulated sowing dates, is greater in October (first sowing date), (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, it is possible to highlight the predominance of the greatest emergence of hairy beggarticks in the third simulated sowing date, observed in the years 2014, 2017, and 2018 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE and F). Until the first half of November (second sowing date), there was predominantly a continuous emergence of hairy beggarticks, while in December (third sowing date), there was a higher speed of emergence. This result can be better observed through the dispersion of points of accumulated emergence observed in the third simulated sowing date (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE and F), as well as by the smaller values of the coefficients \u003cem\u003ea\u003c/em\u003e and \u003cem\u003ec\u003c/em\u003e of the models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The highest density of hairy beggarticks plants occurred in the second simulated soybean sowing date, with an average of 41 plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLongevity and persistence of seed bank\u003c/h2\u003e \u003cp\u003eData analysis showed a significant interaction between burial depth and retrieval moments only for the remaining seeds variable. Meanwhile, in the other variables, germination, dormancy, mortality, and persistence, the simple effect of retrieval moments was verified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For the remaining seeds variable, there was a reduction in the percentage of seeds rescued over the course of the retrieval moments, regardless of the burial depth, where the data was adjusted to the decreasing exponential regression equation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The number of retrieved seeds decreased considerably after 10 months of burial, and in the last evaluation at 16 months, only 3.2, 5.5 and 9% were found for burial depths 0, 3, and 6 cm, respectively, not differing from each other given the overlapping of the confidence intervals of the means.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe hairy beggarticks seeds showed germination close to 80% at retrieval moment 0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Thus, at the time of burial, only 8.3% of the seeds were dormant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). However, it was observed that approximately 43% of the seeds collected at 2 and 4 months after burial germinated when submitted to laboratory testing, approaching zero in subsequent evaluations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Such results are justified in view of the mortality of the remaining seeds, which showed a linear and increasing behavior during the evaluated time (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), reaching approximately 10% after 16 months of evaluation. Thus, almost all seeds did not show viability by the tetrazolium test, giving persistence close to zero after 16 months, for all burial depths (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE), suggesting that the species has a transient seed bank in the soil.\u003c/p\u003e \u003cp\u003eThe seed bank outputs were high based on remaining seeds and mortality data (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and D), predisposing the species to the need for annual restitution of the seed bank, since, after 16 months from burial, an average of 5% of seeds were recovered and presented a low level of germination (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and B). Exits from the hairy beggarticks seed bank can be explained predominantly by the high level of predation and seed decay at the different burial depths evaluated, approaching 100% at a depth of 0 cm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) and about 90% at 6 cm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe dormancy was not a relevant factor for the maintenance of hairy beggarticks seeds in the soil, since it was observed that a maximum of 5% of buried seeds were dormant between 2 and 4 months after burial (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). On the other hand, the mortality of the seeds remained relatively constant, representing the seeds recovered without viability (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTemperature and water potential\u003c/h2\u003e \u003cp\u003eSimilar results were observed in populations of hairy beggarticks from different regions of Brazil, most of which samples germinated at 10 to 35\u0026deg;C (Barros et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Temperature values for maximum germination of hairy beggarticks for an Australian population were estimated with temperatures alternating between 25/15\u0026deg;C or 30/10\u0026deg;C (Chauhan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), or 15\u0026deg;C for populations from different geographic origins (Barros et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the water potential data, the complete inhibition of germination of hairy beggarticks seeds was observed at water potentials below \u0026minus;\u0026thinsp;0.8 MPa through saline solution (Chauhan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), attributing to the enzymatic inhibition necessary for the germination process (Marcos Filho \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). There may be differences when comparing water potentials using saline or PEG solution since NaCl can cause seed toxicity. However, corroborating these results, it was possible to estimate Ѱb for hairy beggarticks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMathematical modeling of the emergence\u003c/h2\u003e \u003cp\u003ePrecipitation associated with the increase in average daily temperature stimulates new emergence of weeds, so this variability in precipitation over the years, with changes in soil water potential, is highly desirable for the development of models based on the region's microclimate (Royo-Esnal et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The models showed very similar predictability for each season, but the model based on the TT predicts emergence in a staggered way, while the TH predicts emergence faster and in a shorter period. Therefore, TH models improve the accuracy of TT model predictions for weed emergence, particularly in locations where periods of water deficit occur (Leguizam\u0026oacute;n et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, the influence of TH may occur for \u003cem\u003eHelianthus annuus\u003c/em\u003e L. even under normal soil water availability conditions (Werle et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The TH model was also more accurate than the TT model for the emergence of \u003cem\u003eConyza bonariensis\u003c/em\u003e L. (Zambrano-Navea et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe highest emergence at the beginning of the crop cycle (first sowing date) are a tendency reported for several weed species, including both poaceae and eudicots (Zandon\u0026aacute; et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). This behavior generally ensures success in the establishment and perpetuation of the species. However, it is difficult to determine exactly the causes of the reduction or increase in the number of weed emergence in each emergence, as several factors may be involved. Among the main aspects, the climatic changes during the seasons and years stand out (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the non-revolving of the soil, the soil cover (oat straw), and the dormancy conditions, which certainly may have interfered with the emergence and establishment of weeds. Furthermore, the density of the species in the area is important information for determining the competitive capacity (Balbinot Jr et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), moreover, it is noteworthy that even at low densities, such as 8 plants per m\u003csup\u003e-2\u003c/sup\u003e, hairy beggarticks can promote up to a 10% reduction in soybean productivity (Rizzardi et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe emergence model for hairy beggarticks seems to be robust enough to be used as a tool in weed management, due to the large data set used. The larger the dataset, the better the parameter estimation. However, models with large and complex data input sets are not always complete and reliable (Colbach et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The benefit of using TH instead of TT is due to its ability to predict possible emergence pauses caused by low soil moisture, which is important to reduce error in practice; i.e., when the emergence model is being applied (Masin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Thus, for the first season, the recommended chemical control would be carried out with the application of a post-emergence herbicide with a residual effect, due to the slower emergence. For the November (second) and December (third) simulated sowing dates, it is necessary to carry out burndown associated with a pre-emergent herbicide to guarantee a residual effect and the initial establishment of the crop free from the presence of weeds. It should also be noted that the models can be used as parameters for planning management practices for several summer crops cultivated in the no-tillage system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLongevity and persistence of seed bank\u003c/h2\u003e \u003cp\u003eThe main source of supply for future weed infestations comes from the seed bank, which presents a dynamic behavior, with inputs coming from immigration or from the seeds produced and dispersed in the area. On the other hand, the outputs occur due to germination, aging, loss of viability, predation, and decay (Chauhan and Johnson \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In the case of the hairy beggarticks under study, seed bank outputs were high based on remaining seeds and mortality data (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and D). Exits from the hairy beggarticks seed bank can be explained predominantly by the high level of predation and seed decay at the different burial depths evaluated.\u003c/p\u003e \u003cp\u003eSimilar results were found for ryegrass (\u003cem\u003eLolium multiflorum\u003c/em\u003e) in which the number of dormant seeds reduced to less than 20% after 60 days of burial (Cechin et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, for horseweed (\u003cem\u003eConyza\u003c/em\u003e sp.), the maximum percentages of dormancy were close to 3 months (Vargas et al. 2016). Due to the results observed for this species, it is possible to conclude that management practices that limit the germination and emergence of the species \u0026ndash; if properly conducted \u0026ndash; are effective due to the reduced persistence of the species in the seed bank in the soil.\u003c/p\u003e \u003cp\u003eThus, the results of the base, optimum, and maximum temperatures for hairy beggarticks emergence are 10.4, 24.7, and 41.9\u0026deg;C, respectively, and the base water potential is -0.85 MPa. Both thermal time and hydrothermal models are suitable for predicting hairy beggarticks emergence. Therefore, they can be used as a tool for decision-making regarding the use of control measures based on environmental conditions. The species exhibits a transient seed bank, with high losses due to predation and decay. Based on the results obtained for this species, it is possible to elaborate management strategies based on climatic conditions that favor the emergence of hairy beggarticks in different sowing times of crops, or that provide the correct positioning of pre- and post-emergence herbicides. In addition, understanding the dynamics of hairy beggarticks seeds in the soil allows the adoption of management strategies that help in the depletion or renewal of the seed bank.\u003c/p\u003e \u003c/div\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExperimental conditions and location\u003c/h2\u003e \u003cp\u003eThe cardinal temperatures and water potential experiments were carried out in the Seed Analysis laboratory of the Faculdade de Agronomia Eliseu Maciel (FAEM/UFPel), in growth chambers (model EL 222, manufacturer ELETROlab\u0026reg;) with a photoperiod of 8/16 hours of light/dark. The growth chamber was composed of six fluorescent lamps with 40 watts. The remaining experiments (modeling the emergence, longevity, and persistence of the hairy beggarticks seed bank) were conducted in field conditions between the years 2014 and 2019.\u003c/p\u003e \u003cp\u003eThe experimental area was the Centro Agropecu\u0026aacute;rio da Palma (CAP), belonging to the Centro de Estudos em Herbologia (CEHERB), Cap\u0026atilde;o do Le\u0026atilde;o \u0026ndash; Rio Grande do Sul (RS), Brazil (31.80\u0026deg;S; 52.50\u0026deg;W). The soil in the area is classified as Ultisol, with a sandy loam texture, belonging to the Pelotas mapping unit (Santos et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Validation of the emergence data was carried out at the Experimental station of Cooperativa Central Ga\u0026uacute;cha Ltda (CCGL), in Cruz Alta \u0026ndash; RS, Brazil (28.60\u0026deg;S; 53.67\u0026deg;W), which has soil classified as Oxisol (Santos 2018). The regions have a humid subtropical climate (class Cfa), being humid temperate, with hot summers and no defined dry season.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSeed collection and origin\u003c/h2\u003e \u003cp\u003eFor temperature and water potential experiments, and longevity of the seed bank the hairy beggarticks seeds were previously collected from different plants with mature terminal capitula from March to April 2016. The seeds were collected from different hairy beggarticks populations, located at CAP, CCGL, and the county of Barra Funda (27.93\u0026ordm;S; 59.04\u0026ordm;W). The seeds from different locations were mixed for use the in experiments, and subsequently evaluated for seed quality (seed germination, viability, and dormancy). The viability of the seeds was evaluated through the tetrazolium test (Mapa \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For experiments of modeling emergence, the seedlings evaluated originated from the seed bank, without previous hairy beggarticks sowing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCardinal temperatures and base water potential\u003c/h2\u003e \u003cp\u003eThree experiments were conducted in a completely randomized design (CRD), with four replications of 50 seeds each, two to determine cardinal temperature and one for water potential. In the first and second experiments, the seeds were exposed to eight constant temperatures, 10, 15, 20, 25, 30, 35, 40, and 45\u0026deg;C. Based on the results, the third experiment to determine the base water potential was conducted using the temperature of 24.7\u0026deg;C. For the experiment, 10 water potentials were tested as treatments: 0, -0.05, -0.1, -0.2, -0.4, -0.6, -0.9, -1.2, -1.5, and \u0026minus;\u0026thinsp;2.0 MPa. For the simulation of water potentials, polyethylene glycol 8000 (PEG) was used in different concentrations to form the solutions (Hardegree and Emmerich \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe seeds were sown on blotting paper, and previously moistened with distilled water (temperature experiment) or PEG solution (water potential experiment). The proportion for water or PEG was 2.5 times the mass of the dry paper placed in acrylic boxes with dimensions of 11 x 11 x 3.5 cm (Mapa \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The germinated seeds were counted and removed daily to better estimate accumulated germination, being considered germinated when the radicle length reached up to 2 mm. Tests were considered complete when no further germination occurred for five days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEmergence modeling\u003c/h2\u003e \u003cp\u003eField experiments were conducted for 5 years (2014 to 2018). For each year the simulated sowing dates were October 20 (first), November 10 (second), and December 1 (third). Prior to trial establishment, the experimental area was planted to oats at 80kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of seed density, with no soil disturbance. Burndown applications with glyphosate (1440 g a.e. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and paraquat (300 g a.i. ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were made 15 and zero days, respectively, before the start of each simulated soybean sowing dates to avoid counting hairy beggartick plants before the start of emergence monitoring.\u003c/p\u003e \u003cp\u003eThe seed bank survey from zero to 5 cm of depth of the area showed 14 different species from 10 distinct botanical families amounting to 73,543 seeds m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (data not shown). By analyzing the composition of the \u003cem\u003eBidens pilosa\u003c/em\u003e seed bank during the trials, an average of 1,416.38 seeds m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e was observed in the first 5 cm of depth. Emergence monitoring of hairy beggarticks from the soil seed bank was carried out every four days for each simulated sowing date up to 24 days, and an additional count at 48 days after October 20, November 10, and December 1 for the different years. The additional count coincided with the total interference prevention period for the soybean crop (Zandon\u0026aacute; et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). The one-hectare (ha) area was divided into four blocks (four repetitions), which received four plots of 15.75 m\u003csup\u003e2\u003c/sup\u003e (3.15 x 5 m) per season. Each plot was monitored with four replicates, and all weeds that emerged in a useful area of 0.25 m\u003csup\u003e2\u003c/sup\u003e were counted. Plants were considered to have emerged when at least one centimeter of their aerial part reached above the ground.\u003c/p\u003e \u003cp\u003eDuring 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\u0026reg;). Soil moisture in the zero- to five-cm layer was determined every four days, at the same time as the emergence counts (Goulart et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). 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. The transformation of moisture percentage data into soil water potential followed the average soil water retention equation proposed by Bortoluzzi et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) for the no-tillage system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLongevity and persistence of the seed bank\u003c/h2\u003e \u003cp\u003eThe experiment started in July 2017 and finished in November 2018. The 125 seeds were distributed in 50g of soil of the Ultisols type and packed in nylon mesh bags (10 x 10 cm), which constituted the experimental units. This number of seeds was selected to obtain 50 viable seeds in the sample. The treatments were arranged in a factorial scheme (3 x 5) in a randomized block design, with four replications. Factor A corresponded to three seed burial depths (0, 3, and 6 cm) and factor B comprised five collection/rescue times (0, 1, 4, 10, and 16 months after the bags were buried), (Vargas et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is noteworthy that for the burial depth of 0 cm, the experimental units were arranged on the soil surface. In the area where the experiment was installed, soybeans were cultivated during the summer, while in the winter the area was kept fallow.\u003c/p\u003e \u003cp\u003eEach season, the collection was carried out by removing the seeds from the bag. Then, they were cleaned by washing the sample with water in a set of sieves of sizes 16, 32, and 60 mesh (Vargas et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). After washing, the sample was left on filter paper to dry for 24 hours and then analyzed in a light stereoscope to extract the remaining seeds (Vargas et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These were submitted to the germination test to determine the physiological quality, as described in Mapa (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Seeds that did not germinate were submitted to the tetrazolium test (Mapa \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) to identify viability. Seeds that did not germinate and were viable were considered dormant, while non-viable seeds after the tetrazolium test were considered dead. Unrecovered seeds were considered predated, decayed, or any other cause of loss, including seed germination.\u003c/p\u003e \u003cp\u003eThe variables analyzed in this experiment were the remaining seeds, germination, dead seeds, and dormant seeds, with the results expressed in percentage (Mapa \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The percentage of remaining seeds was calculated based on the initial number of seeds per repetition, while the percentages of germination, dead, and dormant seeds were calculated based on the number of remaining seeds. Also, persistence was calculated, which expresses the sum of the number of seeds germinated in the laboratory and viable seeds in the tetrazolium test, deducted from the original number of seeds per repetition, and expressed as a percentage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor all experiments, the data obtained were analyzed for normality (Shapiro Wilk test) and homoscedasticity (Hartley test), and subsequently submitted to analysis of variance (p\u0026thinsp;\u0026le;\u0026thinsp;0.05). When statistical significance was found, regression analysis was performed using R software scripts (R Core Team 2012).\u003c/p\u003e \u003cp\u003eFor the temperature and water potential experiments, the cumulative germination rates were determined. For this purpose, the temperature and water potential data were adjusted to the Weibull logistic function (Eq.\u0026nbsp;1), which allowed for estimating the time required to obtain 50% germination for each treatment (T\u003csub\u003e50\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{y=a [1-} {\\text{e}}^{\\text{- (x-T50 +} \\frac{\\text{bln2}\\frac{\\text{1}}{\\text{c}}\\text{ }}{\\text{b}}\\text{)c} }\\text{]}\\)\u003c/span\u003e \u003c/span\u003e [1]\u003c/p\u003e \u003cp\u003ewhere: \u003cem\u003ey\u003c/em\u003e is the percentage of germination; x is the time expressed in days, \u003cem\u003eTT\u003c/em\u003e, or \u003cem\u003eTH\u003c/em\u003e; \u003cem\u003ea\u003c/em\u003e is the maximum recorded emergence percentage; \u003cem\u003eb\u003c/em\u003e is the rate of increase; \u003cem\u003ec\u003c/em\u003e is a shape parameter; and \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e50\u003c/em\u003e\u003c/sub\u003e is the time (days), \u003cem\u003eTT\u003c/em\u003e, or \u003cem\u003eTH\u003c/em\u003e required to achieve 50% germination or emergence.\u003c/p\u003e \u003cp\u003eTo determine the base, optimal and maximum temperatures (Tb, To, and Tmax respectively) the germination rate was estimated at 1/T\u003csub\u003e50\u003c/sub\u003e, and two independent linear regressions (sub- and supra-optimal) were generated, according to the methodology proposed by Dumur et. al (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). The base water potential (Ѱb) was calculated by plotting 1/T\u003csub\u003e50\u003c/sub\u003e for each water potential and determined by the intersection of the regression line with the abscissa (Scherner et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Confidence intervals (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) were calculated using the bootstrap statistical procedure to obtain estimates of Tb, To, and Tmax, whose criterion is the one with the smallest deviation from the residuals (Loddo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These values ​​were used to calculate the thermal time (TT) and hydrothermal time (TH) for each combination of temperature and water potential in the field.\u003c/p\u003e \u003cp\u003eFor field-condition emergence modeling experiments, the data were converted to cumulative emergence, based on the total emergence of emerged seedlings. Likewise, soil temperature and moisture data were used to determine thermal (TT) and hydrothermal (TH) time, according to the methodology proposed by Gummerson (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Then, the relationship between cumulative emergence was described by the Weibull model (Eq.\u0026nbsp;1). The developed model was validated with emergence data from the experimental station of the CCGL company, and the actual values ​​of emergence and the values ​​estimated by the model were analyzed by mean squared error (MSE), (Mayer and Butler \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Roman et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and by the Akaike Information Criterion (AIC), (Qi and Zhang \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the seed bank evaluation experiment, regression analysis was modeled for the retrieval moments. The remaining seed variables, germination, dormancy, and persistence fit into the three-parameter decreasing exponential regression equation (Eq.\u0026nbsp;2):\u003c/p\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;y0\u0026thinsp;+\u0026thinsp;a*e(-b*x) [2]\u003c/p\u003e \u003cp\u003ewhere: \u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;response variable of interest; \u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;collection times; \u003cem\u003ee\u003c/em\u003e\u0026thinsp;=\u0026thinsp;exponential function; \u003cem\u003ey0\u003c/em\u003e\u0026thinsp;=\u0026thinsp;is the intercept (response value for x\u0026thinsp;=\u0026thinsp;0); \u003cem\u003ea\u003c/em\u003e\u0026thinsp;=\u0026thinsp;difference between the maximum and minimum points of the variable; and \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;slope of the curve.\u003c/p\u003e \u003cp\u003eA linear polynomial regression equation was the most appropriate for the data on the mortality variable (Eq.\u0026nbsp;3):\u003c/p\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;a\u0026thinsp;+\u0026thinsp;bx [3]\u003c/p\u003e \u003cp\u003ewhere: \u003cem\u003ey\u003c/em\u003e\u0026thinsp;=\u0026thinsp;response variable of interest; \u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;collection times; \u003cem\u003ea\u003c/em\u003e\u0026thinsp;=\u0026thinsp;is the intercept or linear coefficient; and \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;represents the slope of the line.\u003c/p\u003e \u003cp\u003eThe state of the seeds in the soil in time (months) was estimated by the different variables: germination, mortality, viability (viable and non-viable seeds), and predation or deterioration of the seeds, based on the averages.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to National Research Council (CNPq) (Proc. 308363/2018-3) for the research fellowships granted to several of the authors with of undergraduate and graduate scholarships during the course of this work.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmaral A, Takaki M (1998) Achene dimorphism in \u003cem\u003eBidens pilosa\u003c/em\u003e L. as determined by germination test. 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Planta Daninha. http://doi.org/10.1590/S0100-8358201836010004\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biological parameters, hydrothermal population dynamics, temperature, water potential","lastPublishedDoi":"10.21203/rs.3.rs-3959684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3959684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo develop models capable of predicting the emergence of hairy beggarticks and assist integrated management, it is fundamental to have knowledge of the environmental factors that influence the germination of the species. The objective of this study was to estimate temperature and water potential cardinals for hairy beggarticks germination, the longevity of its seed bank, and to develop a model of its emergence in the field. Experiments were carried out in the laboratory to determine the temperature and water potential base for seed germination. Eight different temperatures (10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, and 45.0\u0026deg;C), and 10 different water potentials (0, -0.05, -0.1, -0.2, -0.4, -0.6, -0.9, -1.2, -1.5, and \u0026minus;\u0026thinsp;2.0 MPa) were tested. The field experiments were conducted between 2014 and 2019 using three monitoring seedling emergences. To evaluate the longevity and persistence of the seed bank, a factorial experiment was conducted with three burial depths (0, 3, and 6 cm) and five seed retrieval moments (0, 1, 4, 10, and 16 months). Base, optimal, and maximum temperatures for hairy beggarticks germination are 10.4\u0026deg;C, 24.7\u0026deg;C, and 41.8\u0026deg;C, respectively. The base water potential for the emergence of hairy beggarticks is -0.85 MPa. The thermal and hydrothermal time models are adequate to predict the emergence of hairy beggarticks in different soybean sowing dates. The species has a transient seed bank, however, the greater the seed burial depth, the greater the longevity of the soil seed bank.\u003c/p\u003e","manuscriptTitle":"Features of germination, emergence modeling, longevity, and persistence in Bidens pilosa seed bank","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-19 06:33:11","doi":"10.21203/rs.3.rs-3959684/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f99bf294-3d16-46ef-a76c-bc697deb3f8e","owner":[],"postedDate":"February 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-26T16:23:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-19 06:33:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3959684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3959684","identity":"rs-3959684","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}