Understanding the timing of weed emergence is crucial to effective management. Management practices implemented too early may fail to completely control late-emerging seedlings, whereas management practices implemented too late will suffer from low efficacy. Weed emergence times reflect biological factors, such as seed dormancy and germination requirements, as well as environmental conditions. We conducted a systematic review of studies that developed models to predict weed emergence temporal patterns. We screened 1,854 studies, 98 of which were included in the final dataset. Most of the studies included were conducted in North America (51%) or Europe (30%). A wide variety of weed species (102) and families (21) were included, and many studies modeled several weeds. Grass weeds (Poaceae) were modeled most frequently (83 instances). Most weeds (40%) had base temperature ${T_{\rm{b}}}$ values between 0 and 5 C, and 38% had base water potential ${{\rm{\psi }}_{\rm{b}}}$ ranging from −1.0 to −0.5 MPa. Most studies used empirical parametric models, such as Weibull (40%) or Gompertz (30%) models. Nonparametric and mechanistic models were also represented. Models varied in their biological and environmental data requirements. In general, empirical parametric models based on hydrothermal time (i.e., time above base temperature and water potential thresholds) represented a good balance between ease of use and prediction accuracy. Soft computing approaches such as artificial neural networks demonstrated substantial potential in situations with complex emergence patterns and limited data availability, although they (soft computing approaches) can be susceptible to overfitting. Our study also demonstrated variability in model performance and limited generalizability across species and regions. This finding underscores the need for context-specific and well-validated weed emergence models to inform management, especially in the context of climate change.

Late-season weed emergence in California rice fields complicates decisions concerning the timing of control measures. The objective of this study was to predict the emergence of three problematic weed species in rice using thermal time models. Smallflower umbrella sedge, barnyardgrass, and bearded sprangletop seedlings were counted and removed daily at three locations across the Sacramento Valley rice-growing region in 2018. The accumulation of thermal time (growing degree days; GDD) commenced with the initial flooding of the fields at each location, utilizing the specific base temperatures corresponding to each species. The pattern of emergence as a function of GDD was modeled with a Weibull function. Root-mean-square values for comparing actual and model-predicted cumulative emergence values were 6% to 23%. Percent cumulative emergence initially increased rapidly for smallflower umbrella sedge and reached 90% emergence with accumulation of 13 GDD. Barnyardgrass emerged after smallflower umbrella sedge and reached 90% emergence with an accumulation of 124 GDD. Bearded sprangletop had a delay of 64 GDD compared to barnyardgrass to reach first emergence and reached 90% emergence at 215 GDD. The period of weed emergence at all field sites differed across the three species and led to a continuous spectrum of weed emergence over time. This study characterizes the emergence of three economically important rice weeds and provides useful information for the timing of weed management. Typical herbicide applications on the day of seeding may have less efficacy on the late-emerging weeds, causing reduced weed control. Delayed herbicide application, overlay of residual herbicides, or use of herbicides with longer residual activity are suggested to control late-emerging weeds.

Horseweed is a North American indigenous plant species commonly found in Nebraska cropping systems. Horseweed management is challenging because of horseweed’s prolific seed production, long-distance seed dispersal via wind, competitiveness, and rapid evolution of herbicide resistance. Understanding the horseweed emergence pattern across Nebraska can contribute to implementing effective and more sustainable tactics to minimize its impact on cropping systems. Field studies were conducted during fall and spring from 2016 to 2018 in Lincoln (corn and soybean), North Platte (wheat stubble and soybean), and Scottsbluff (corn and fallow) to investigate the emergence pattern of horseweed accessions from Lincoln, North Platte, and Scottsbluff, NE. Results show that most horseweed seedling emergence occurred in fall (99%) and only a few seedlings emerged in spring across locations, except in the wheat stubble experiment at North Platte, where higher spring emergence was detected (3% to 22%). In four out of six experiments, the density of total emerged seedlings of each accession was greatest when established in their site of origin. Our results suggest that late fall and/or early spring is likely the best timing for horseweed management across Nebraska.

Silverleaf nightshade (Solanum elaeagnifolium Cav.) has become a highly troublesome weed in irrigated summer crops in Israel. Because herbicide-based options to control this weed are limited, the best way to improve weed control is through a study of its biology, particularly its germination dynamics. The main objective of this study was to determine the impact of temperature on the seed germination dynamics of S. elaeagnifolium and to develop a temperature-based (thermal) prediction model for three S. elaeagnifolium populations growing in different ecosystems in Israel. To this end, a laboratory study was undertaken in which the germination proportion of S. elaeagnifolium seeds was monitored under seven temperature regimes: 2/8, 7/13, 12/18, 17/23, 22/28, 27/33, and 32/38 C (night/day). In addition, the impact of alternating temperature regimes between night and day temperatures (of 0, 2, 4, 6, 8, and 10 C), averaged over 20 and 25 C, was determined. It was found that the three populations shared similar germination characteristics and dynamics. An alternation of ≥6 C between night and day temperatures was needed for optimal germination, with no germination taking place under constant temperatures. In all three populations, the minimal requirement for germination was a 12/18 C (night/day) regime, with the final germination proportion lying between 0.25 and 0.36. The highest final germination proportion of ≥0.8 was observed for the 17/23 C regime in all three populations. Modeling the germination rate as a function of temperature allowed us to determine cardinal temperatures for all three populations taken together, with the values being Tb = 10.8 C (base temperature), To = 23.8 C (optimal temperature), and Tc = 35.9 C (ceiling temperature). These biological parameters allowed accurate (root mean-square error < 0.06%) prediction of S. elaeagnifolium seed germination over the entire temperature range.

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

Regeneration from seed affects species assembly in plant communities, and temperature is the most important environmental factor controlling the germination process. Thermal dependence of seed germination is thus associated with species occurrence in an ecosystem. Hence, we aimed to investigate the role of temperature on seed germination of ten tree species from the western Brazilian Amazon. Seeds were collected in the state of Rondônia, Brazil, and set to germinate under constant temperatures ranging from 10 to 40°C in germination chambers. We calculated germination capacity (G%), germination rate (GR50, reciprocal of germination time), and thermal parameters, such as cardinal temperatures and thermal time requirements. Most species had a large range of temperatures showing G% ≥80%, with optimal temperature varying from 20 to 40°C. Base temperature ranged from 6 to 12°C and ceiling temperatures were mainly >40°C. Astronium lecointei and Parkia nitida showed high germination capacity under temperatures of 35–40°C, while germination of Theobroma cacao dropped from 100% to zero under temperatures between 37 and 40°C. The climax species Cedrela fissilis had the slowest germination time (10 days) and highest thermal time requirement, while seeds of Enterolobium schomburgkii (a late-successional species) germinated within the first day of the experiment. Rapid recruitment of Amazon species could be favoured with treefall disturbance, which increases temperatures in the understory, but sharp limits might be found in the supra-optimal range of temperatures. Such patterns might indicate different regeneration strategies in the tropical rainforest, providing important information regarding seed germination among Amazon species.

Though the number of climate-change-related agro-phenological investigations are growing rapidly, the attention paid to spring crops has been much less than to winter ones. The objective of the current study was to investigate long-term temporal and spatial trends of spring barley phenology and to project changes in the timing and duration of different phenological phases during the current century. Higher temperatures significantly affected the potential scheduling of agricultural practices, accelerating the occurrence of sowing and emergence dates. Historical trends in harvest dates of spring barley showed a slight delay. These changes resulted in the extension of the total vegetative period of spring barley by >12 days over the period investigated (1961–2015). Since Lithuania is situated on the Baltic Sea, an increase in temperature along with an increase in distance from the sea was characteristic over the last 55 years. Projected changes in the occurrence of phenological phases of spring barley differ significantly from analysed historical changes and advancement of all phenological phases have been projected according to both Representative Concentration Pathway (RCP) 2.6 and RCP 8.5 climate change scenarios. Shortening of the total vegetative period by 5 days is foreseen for the far (2071–2100) future according to the pessimistic (RCP 8.5) climate change scenario.

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

Little is known about the biology and control of hophornbeam copperleaf, a weed of increasing importance in the Midwest. More than 2 wk of cold stratification and a 0.2% KNO3 solution increased germination of hophornbeam copperleaf. Germination at constant 30 C was 47% and alternating 30/20 C was 65%. Scarification did not increase hophornbeam copperleaf germination. Within the first 600 growing degree days after soybean planting, plant height, leaf area, and dry matter accumulation of hophornbeam copperleaf grown in soybean and alone were similar. Subsequently, leaf area and dry matter accumulation of hophornbeam copperleaf grown alone were greater than of those grown in soybean. In contrast, plant height of hophornbeam copperleaf grown in soybean was greater than when grown alone. Hophornbeam copperleaf grown alone produced up to 12 510 seeds/plant, whereas hophornbeam copperleaf grown with soybean produced 980 seeds/plant. Of 13 postemergent herbicides evaluated on hophornbeam copperleaf in soybean, only lactofen, acifluorfen, and fomesafen controlled 80% or more. Lactofen at 210 g ai/ha consistently controlled more than 95% of the hophornbeam copperleaf.

Growth and development of bigroot morningglory was observed and quantified. Emergence occurred 75 ± 5 growing degree days (GDD) after seeding. Flower and seed production began 630 ± 20 GDD after emergence and continued until the first frost killed the shoots. Seedlings needed approximately 460 GDD of growth to become perennial. In the second year of growth, plants emerged in early May and flowered within 425 ± 50 GDD. Shoot dry weight accumulation in first-year plants was 3.5 g for the first 600 GDD after which a fifteenfold increase in dry weight occurred. Root growth followed the same pattern, however the large increase in dry weight occurred approximately 300 GDD later than that of the shoots. The root:shoot ratio was 0.2 to 0.3 for the first 900 GDD and increased to greater than 1.0 by the final harvest.

Understanding the environmental variables influencing the phenological development of weeds is essential for simulation model development. Temperature and photoperiod are important variables governing the phenological development of weeds. Growth cabinet studies were conducted to characterize the phenological development of Setaria viridis in response to variations in temperature and photoperiod and to determine the duration of the juvenile phase and the effect of temperature and photoperiod on reproductive development. Setaria viridis was adapted to a temperature range from 6.5 to 47 C. Phenological development of S. viridis was described accurately in terms of thermal days (cumulative day degrees above a base temperature) and biological days (Bd: chronological days at the optimum temperature and photoperiod). Four developmental phases of S. viridis were described: (1) a juvenile (photoperiod insensitive) phase of 2.6 Bd; (2) a photoperiod-sensitive inductive phase of 2.2 Bd; (3) a photoperiod-sensitive postinductive phase of 6.0 Bd; and (4) a photoperiod-insensitive inductive phase of 10.9 Bd. Photoperiod sensitivity of S. viridis did not differ with stage of development when expressed as a rate. Interpretation of constant sensitivity to photoperiod will simplify simulation of weed phenology in mechanistic models.

Understanding how plant traits interact with climate to determine plant niches is decisive for predicting climate change impacts. While lifespan and seed size modify the importance of germination timing, germination traits such as base temperature and base water potential directly translate climatic conditions into germination timing, impacting performance in later life stages. Yet we do not know how base temperature, base water potential, seed mass, lifespan and climate are related. We tested the relationships between base temperature and base water potential for germination, seed size and lifespan while controlling for bioclimatic regions. We also quantified the phylogenetic signal in germination traits and seed size using Pagel's λ. We used a worldwide data set of germination responses to temperature and moisture, seed size and lifespan of 240 seed plants from 49 families. Both germination temperature and moisture are negatively related to seed size. Annual plants show a negative relation between seed size and base water potential, whereas perennials display a negative relation between base temperature and seed mass. Pagel's λ highlighted the slow evolution of base temperature for germination, comparable to seed mass while base water potential was revealed to be labile. In the future, base water potential and seed mass can be used when moisture niches of plants are to be predicted. Lifespan, seed size and base temperature should be taken into account when analysing thermal limits of species distributions.

Studies were conducted to calibrate and validate a mathematical model previously developed to predict common lambsquarters seedling emergence at different corn seedbed preparation times. The model was calibrated for different types of soil by adjusting the base temperature of common lambsquarters seedling emergence to the soil texture. A relationship was established with the sand mineral fraction of the soil and was integrated into the model. The calibrated model provided a good fit of the field data and was accurate in predicting cumulative weed emergence in different soil types. The validation was done using data collected independently at a site located 80 km from the original experimental area. There were no differences between observed and predicted values. The accuracy of the model is very satisfactory because the emergence of common lambsquarters populations was accurately predicted at the 95% probability level. This model is one of the first to take into consideration seedbed preparation time and soil texture. This common lambsquarters emergence model could be adapted to model other weed species whose emergence is limited by low spring temperature.

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

Littleseed canarygrass is a troublesome grass weed in wheat fields in Iran. Predicting weed emergence dynamics can help farmers more effectively control weeds. In this work, four nonlinear regression models (beta, three-piece segmented, two-piece segmented, and modified Malo's exponential sine) were compared to describe the cardinal temperatures for the germination of littleseed canarygrass. Two replicated experiments were performed with the same temperatures. An iterative optimization method was used to calibrate the models and different statistical indices (mean absolute error [MAE], coefficient of determination [R2], intercept and slope of the regression equation of predicted vs. observed hours to germination) were applied to compare their performance. The three-piece segmented model was the best model to predict the germination rate (R2 = 0.99, MAE = 0.20 d, and coefficient of variation 1.01 to 4.06%). Based on the model outputs, the base, the lower optimum, the upper optimum, and the maximum temperatures for the germination of littleseed canarygrass were estimated to be 4.69, 22.60, 29.62, and 38.13 C, respectively. The thermal time required to reach 10, 50, and 90% germination was 31.98, 39.26 and 45.55 degree-days, respectively. The cardinal temperatures depended on the model used for their estimation. Overall, the three-piece segmented model was better suited than the other models to estimate the cardinal temperatures for the germination of littleseed canarygrass.

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

Soybean cyst nematode (SCN) is a major soybean yield–limiting disease in the United States. Henbit, a winter annual species common to no-till fields in the midwestern United States, is known to act as an alternative host for SCN. A simulation was performed to estimate how likely SCN was to reproduce on henbit roots during a 30-yr period in two important soybean production areas of Nebraska. Simulations were conducted using published information on henbit seedling emergence, SCN reproduction on henbit roots, and SCN response to soil temperature. Results indicate that SCN would be able to complete one generation on henbit roots under Nebraska conditions. The SCN reproductive cycle was not likely to be completed before the winter in south central Nebraska, but one SCN generation was predicted to be completed in the fall in 2 out of 30 simulation years (7% likelihood) in southeast Nebraska. Based on our predictions, to reduce the chances of SCN population build-up in the absence of its main host (soybean), weed management in fields infested with both henbit and SCN should be completed after crop harvest in the fall when most henbit seedlings have emerged and are growing but the SCN developing on henbit roots have not yet achieved full maturity in Nebraska.

First- and second-year seedbank emergence of 23 summer annual weed species common to U.S. corn production systems was studied. Field experiments were conducted between 1996 and 1999 at the Iowa State University Johnson Farm in Story County, Iowa. In the fall of 1996 and again in 1997, 1,000 seeds for most species were planted in plastic crates. Seedling emergence was counted weekly for a 2-yr period following seed burial (starting in early spring). Soil temperature at 2 cm depth was estimated using soil temperature and moisture model software (STM2). The Weibull function was fit to cumulative emergence (%) on cumulative thermal time (TT), hydrothermal time (HTT), and day of year (DOY). To identify optimum base temperature (Tbase) and base matric potential (ψbase) for calculating TT or HTT, Tbase and ψbase values ranging from 2 to 17 C and −33 to −1,500 kPa, respectively, were evaluated for each species. The search for the optimal model for each species was based on the Akaike's Information Criterion (AIC), whereas an extra penalty cost was added to HTT models. In general, fewer seedlings emerged during the first year of the first experimental run (approximately 18% across all species) than during the second experimental run (approximately 30%). However, second-year seedbank emergence was similar for both experimental runs (approximately 6%). Environmental effects may be the cause of differences in total seedling emergence among years. Based on the AIC criterion, for 17 species, the best fit of the model occurred using Tbase ranging from 2 to 15 C with four species also responding to ψbase = −750 kPa. For six species, a simple model using DOY resulted in the best fit. Adding penalty costs to AIC calculation allowed us to compare TT and HTT when both models behaved similarly. Using a constant Tbase, species were plotted and classified as early-, middle-, and late-emerging species, resulting in a practical tool for forecasting time of emergence. The results of this research provide robust information on the prediction of the time of summer annual weed emergence, which can be used to schedule weed and crop management.

Base temperature (Tb) for leaf appearance and the thermal time interval between appearance of successive leaves (phyllochron) were determined for six common weed species from a series of growth chamber experiments. Mean leaf appearance Tb values for giant ragweed, velvetleaf, redroot pigweed, large crabgrass, woolly cupgrass, and wild-proso millet were 1.3, 8.0, 8.5, 4.5, 2.2, and 5.1 C, respectively, and mean phyllochron values were 37.2, 34.4, 17.3, 42.2, 65.2, and 34.2 growing degree-days per leaf, respectively. Phyllochron values increased slightly with mean temperature for each weed species. To our knowledge, these are the first reported leaf appearance Tb and phyllochron values for giant ragweed, woolly cupgrass, and wild-proso millet. The results confirm leaf appearance Tb and phyllochron values reported previously for velvetleaf and redroot pigweed. However, large crabgrass leaf appearance Tb and phyllochron values in our study varied somewhat from those reported previously. The results provide information needed for parameterization of plant growth models that predict leaf development based on thermal time.

A mathematical model was developed to predict common lambsquarters seedling emergence in southwestern Quebec. The model was based on the thermal-time concept, using air temperatures in the double-sine calculation method. The model was built using data from five experiment-years for corn naturally infested with weed populations. Once developed, the model was calibrated using different crop seedbed preparation times. The base temperature was then adjusted for each time of seedbed preparation. A power regression function was used to relate adjusted base temperatures and the accumulated thermal units at seedbed preparation time. A modified Weibull function was then fitted to the field emergence data, expressed as the cumulative proportion of the total seedling emergence over the growing season as a function of cumulative thermal units. The simplicity and accuracy of this model would make it an excellent tool to predict common lambsquarters seedling emergence in field situations, facilitating the determination of the timing of scouting in integrated weed management systems.