Skip to main content Accessibility help
×
Home
Hostname: page-component-78dcdb465f-fqvcn Total loading time: 0.775 Render date: 2021-04-17T20:08:28.517Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false, "newCitedByModal": true }

Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species

Published online by Cambridge University Press:  04 November 2020

Lauren M. Schwartz-Lazaro
Affiliation:
Assistant Professor, School of Plant, Environmental, and Soil Sciences, Louisiana State University AgCenter, Baton Rouge, LA, USA; former institutional affiliation: University of Arkansas, Fayetteville, AR, USA
Lovreet S. Shergill
Affiliation:
Assistant Professor, Montana State University, Southern Agricultural Research Center, Huntley, MT, USA; former institutional affiliations: U.S. Department of Agriculture, Agricultural Research Service, Beltsville, MD, USA; and Department of Plant and Soil Sciences, University of Delaware, Georgetown, DE, USA
Jeffrey A. Evans
Affiliation:
Farmscape Analytics, Concord, NH, USA
Muthukumar V. Bagavathiannan
Affiliation:
Associate Professor, Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, USA
Shawn C. Beam
Affiliation:
Graduate Research Assistant, School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA
Mandy D. Bish
Affiliation:
Extension Specialist, Division of Plant Sciences, University of Missouri, Columbia, MO, USA
Jason A. Bond
Affiliation:
Research/Extension Professor, Delta Research and Extension Center, Mississippi State University, Stoneville, MS, USA
Kevin W. Bradley
Affiliation:
Professor, Division of Plant Sciences, University of Missouri, Columbia, MO, USA
William S. Curran
Affiliation:
Professor, Penn State University, University Park, PA, USA
Adam S. Davis
Affiliation:
Professor and Head, Department of Crop Sciences, University of Illinois, Urbana, IL, USA; former institutional affiliation: U.S. Department of Agriculture, Agricultural Research Service, Urbana, IL, USA
Wesley J. Everman
Affiliation:
Associate Professor, Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, USA
Michael L. Flessner
Affiliation:
Assistant Professor, School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA
Steven C. Haring
Affiliation:
Graduate Research Assistant, School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA
Nicholas R. Jordan
Affiliation:
Professor, Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, USA
Nicholas E. Korres
Affiliation:
ORISE Research Scientist, U.S. Department of Agriculture, Agricultural Research Service, Urbana, IL, USA; former institutional affiliation: University of Arkansas, Fayetteville, AR, USA
John L. Lindquist
Affiliation:
Professor, Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE, USA
Jason K. Norsworthy
Affiliation:
Professor and Elms Farming Chair of Weed Science, Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, USA
Tameka L. Sanders
Affiliation:
Research Associate II, Delta Research and Extension Center, Mississippi State University, Stoneville, MS, USA
Larry E. Steckel
Affiliation:
Professor, Department of Plant Sciences, University of Tennessee, Jackson, TN, USA
Mark J. VanGessel
Affiliation:
Professor, Department of Plant and Soil Sciences, University of Delaware, Georgetown, DE, USA
Blake Young
Affiliation:
Graduate Research Assistant, Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, USA
Steven B. Mirsky
Affiliation:
Research Ecologist, U.S. Department of Agriculture, Agricultural Research Service, Beltsville, MD, USA
Corresponding
Rights & Permissions[Opens in a new window]

Abstract

Potential effectiveness of harvest weed seed control (HWSC) systems depends upon seed shatter of the target weed species at crop maturity, enabling its collection and processing at crop harvest. However, seed retention likely is influenced by agroecological and environmental factors. In 2016 and 2017, we assessed seed-shatter phenology in 13 economically important broadleaf weed species in soybean [Glycine max (L.) Merr.] from crop physiological maturity to 4 wk after physiological maturity at multiple sites spread across 14 states in the southern, northern, and mid-Atlantic United States. Greater proportions of seeds were retained by weeds in southern latitudes and shatter rate increased at northern latitudes. Amaranthus spp. seed shatter was low (0% to 2%), whereas shatter varied widely in common ragweed (Ambrosia artemisiifolia L.) (2% to 90%) over the weeks following soybean physiological maturity. Overall, the broadleaf species studied shattered less than 10% of their seeds by soybean harvest. Our results suggest that some of the broadleaf species with greater seed retention rates in the weeks following soybean physiological maturity may be good candidates for HWSC.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Weed Science Society of America

Introduction

Farmers have relied on chemical weed control in row-crop production for decades, but the ongoing success of chemical tactics has been hindered by the evolution of resistance to a broad range of herbicide chemistries in many weed species (Heap Reference Heap2019). The only certain way to break this evolutionary process is to ensure that no individual weeds contribute seeds to future generations (Palumbi Reference Palumbi2001). Weeds that escape management and do retain seeds at harvest are at a potential risk of evolving in ways that can impact how and when weed seeds enter into the soil seedbank, such as earlier shattering potential or more prostrate growth habit. Although herbicides remain the most cost-effective tools to manage weeds, new management practices are urgently needed as weeds continue to develop herbicide resistance (Heap Reference Heap2019), and herbicide-resistance evolution has outpaced new herbicide commercialization for decades.

Weed seedbanks act as a primary source of annual weed infestations (Buhler et al. Reference Buhler, Hartzler and Forcella1997; Gill and Holmes Reference Gill and Holmes1997). Therefore, reduction in seedbank replenishment is critical for effective weed management (Gallandt Reference Gallandt2006; Haring and Flessner Reference Haring and Flessner2018; Schwartz-Lazaro and Copes Reference Schwartz-Lazaro and Copes2019). Weeds that survive chemical and other control tactics or emerge after treatment are likely to disperse seed into the soil seedbank. Harvest weed seed control (HWSC) captures and processes the unshattered seed retained by weeds at harvest time to minimize the number of viable seeds dispersed to the soil. HWSC tactics were first developed and adopted in Australia and include practices such as narrow windrow burning, bale direct, chaff tramlining or lining, and seed impact mills, such as the vertical integrated Harrington Seed DestructorTM (vertical iHSD, de Bruin Engineering, Mount Gambier, SA, Australia), Seed TerminatorTM (Seed Terminator, Tonsley, SA, Australia), or RedekopTM (Redekop Manufacturing, West Saskatoon, SK, Canada) system (Walsh et al. Reference Walsh, Harrington and Powles2012, Reference Walsh, Broster, Schwartz-Lazaro, Norsworthy, Davis, Tidemann, Beckie, Lyon, Soni, Neve and Bagavathiannan2018; Walsh and Powles Reference Walsh and Powles2007). An additional HWSC tactic is the chaff cart, which was first developed in Canada and later refined and adopted in Australia. HWSC has the potential to prevent large proportions of viable seeds that remain on the weed plants at the time of harvest from entering the seedbank, substantially reducing the total seed rain of weeds, including herbicide-resistant weeds. Therefore, HWSC can negatively impact weed population dynamics by limiting seed addition and preventing the buildup of resistant subpopulations in the soil seedbank over time. Reducing viable weed seed additions to the soil seedbank by greater than 40% can reduce weed population growth and increase net returns to weed management (Liebman and Davis Reference Liebman and Davis2009). However, Tidemann et al. (Reference Tidemann, Hall, Harker and Alexander2016) showed that 80% HWSC efficacy is required to reduce wild oat (Avena fatua L.) seedbank size.

Different HWSC systems aid in effectively managing 60% to 99% seed of various weed species in Australian production systems (Walsh et al. Reference Walsh, Newman and Powles2013), but the efficacy of these systems depends upon the proportion of weed seed retained on the weed plant at crop harvest, because only those seeds will be captured and thus removed or processed by the harvest machinery (Gill and Holmes Reference Gill and Holmes1997; Walsh et al. Reference Walsh, Broster, Schwartz-Lazaro, Norsworthy, Davis, Tidemann, Beckie, Lyon, Soni, Neve and Bagavathiannan2018). During crop harvest in the U.S. Corn Belt, seeds produced by many annual weed species remain undispersed on the mother plant and therefore have the potential to be harvested along with the grain crop (Davis Reference Davis2008; Norsworthy et al. Reference Norsworthy, Walsh, Bagavathiannan, Bradley, Steckel, Kruger, Loux, Eubank, Davis and Johnson2014).

Palmer amaranth (Amaranthus palmeri S. Watson), common waterhemp [Amaranthus tuberculatus (Moq.) Sauer], giant ragweed (Ambrosia trifida L.), common lambsquarters (Chenopodium album L.), and morningglory species (Ipomoea spp.) are the most problematic broadleaf weeds in corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] production systems in the United States (Van Wychen Reference Van Wychen2015, Reference Van Wychen2016). A large proportion (>50%) of seeds are retained in these weed species concurrent with the crop harvest window (Davis Reference Davis2008; Goplen et al. Reference Goplen, Sheaffer, Becker, Coulter, Breitenbach, Behnken, Johnson and Gunsolus2016; Schwartz-Lazaro et al. Reference Schwartz-Lazaro, Norsworthy, Young, Bradley, Kruger, Davis, Steckel and Walsh2016, Reference Schwartz-Lazaro, Green and Norsworthy2017a). Goplen et al. (Reference Goplen, Sheaffer, Becker, Coulter, Breitenbach, Behnken, Johnson and Gunsolus2016) observed that A. trifida retained on average 80% of total seeds produced by the time 75% of soybean were already harvested in Minnesota. High seed retention ranging from 95% to 100% in A. palmeri and A. tuberculatus at soybean maturity was reported in a survey across Arkansas, Tennessee, Illinois, Missouri, and Nebraska (Schwartz-Lazaro et al. Reference Schwartz-Lazaro, Norsworthy, Young, Bradley, Kruger, Davis, Steckel and Walsh2016). Davis (Reference Davis2008) reported that ivyleaf morningglory (Ipomoea hederacea Jacq.) retained 75% and 85% of its seed in corn and soybean fields, respectively, in east-central Illinois. Studies examining weed seed retention at crop harvest in Australia and Canada have also reported a high proportion (>70 %) of seed retention at crop harvest in broadleaf weeds such as wild radish (Raphanus raphanistrum L.), common sowthistle (Sonchus oleraceus L.), flaxleaf fleabane [Conyza bonariensis (L.) Cronquist], African turnip weed (Sisymbrium thellungii O.E. Schulz), cleavers (Galium spp.), and wild mustard (Sinapis arvensis L.) (Burton et al. Reference Burton, Beckie, Willenborg, Shirtliffe, Schoenau and Johnson2016; Walsh and Powles Reference Walsh and Powles2014; Widderick et al. Reference Widderick, Keenan and Walsh2014). Furthermore, Bitarafan and Andreasen (Reference Bitarafan and Andreasen2020) showed that, on average, 260, 195, 411, and 316 seeds plant-1 were produced by black bindweed [Fallopia convolvulus (L.) Á. Löve], wild mustard (Sinapis arvensis L.), corn spurry (Spergula arvensis L.), and chickweed [Stellaria media (L.) Vill.], respectively, of which an average 44%, 67%, 45%, and 56% of the seeds were retained on the plants at spring oat (Avena sativa L.) harvest. However, the level of seed retention in a species is likely to be influenced by agroecological and environmental factors (Shirtliffe et al. Reference Shirtliffe, Entz and Van Acker2000; Taghizadeh et al. Reference Taghizadeh, Nicolas and Cousens2012). Little research has been conducted to evaluate seed retention of various economically important weeds in major U.S. grain-producing regions that currently face multiple herbicide-resistant weed infestations. To address this, we conducted studies to determine the proportion of weed seeds shattered versus retained relative to the date of soybean physiological maturity of 13 economically important broadleaf weeds across the United States. These studies aid in determining the potential for successful use of HWSC in these three major U.S. grain-producing regions.

Materials and Methods

Study Sites

We outlined a common research protocol that included 14 states spread across the southern, northern, and mid-Atlantic United States. Field experiments were conducted in 2016 and 2017, except for Pennsylvania and Tennessee, which only participated in 2016. Each location planted soybeans using local standard practices described in local extension bulletins, including variety, seeding rate, row spacing, fertility, and other practices, and collected information on planting date, physiological maturity progression, and harvest date (Table 1).

Table 1. Soybean planting, physiological maturity, and harvest dates for each region and state in 2016 and 2017.

a Regions include South-Central (SC): Arkansas (AR), Mississippi (MS), Tennessee (TN), and Texas (TX); North-Central (NC): Illinois (IL), Michigan (MI), Minnesota (MN), Missouri (MO), and Nebraska (NE); and Mid-Atlantic (MA): Delaware (DE), Maryland (MD), North Carolina (NC), Pennsylvania (PA), and Virginia (VA).

b NA, unavailable data.

Data Collection

Within each location, at least three locally problematic broadleaf or grass weed species were chosen for study, and a total of 16 broadleaf species were investigated across locations. Grass species are presented separately in a sister paper (Schwartz-Lazaro et at. Reference Schwartz-Lazaro, Shergill, Evans, Bagavathiannan, Beam, Bish, Bond, Bradley, Curran, Davis, Everman, Flessner, Harring, Jordan, Korres, Lindquist, Norsworthy, Sanders, Steckel, VanGessel, Young and Mirsky2021). Shortly after soybean emergence, individual plants were marked with flags for study. At least 10 individuals of each species were selected at each location, but the number ranged from 10 to 25. Weeds that did not emerge from the soil seedbank were either seeded or transplanted into the crop. Transplanted weeds were of the same growth stage as those in the study field to mimic similar germination dates. The soybean was kept free of other weeds throughout the growing season by covering the target weeds with buckets and then applying a herbicide POST broadcast. Non-target weeds that were not controlled by this application were removed by hand throughout the growing season. The target weeds used in the study were allowed to compete with the soybean crop until they began to flower. Once the target weeds began to flower, all soybean plants within 2 m were removed to ensure that any shattered seed would fall into the seed trays. Four seed-collection trays (F1721 Tray, T.O. Plastics, Clearwater, MN) measuring 0.2 m2 each were placed around the bottom of each target plant to collect any seed shed from the plant. If a plant spread over the outer edges of the trays during the course of the study, it was trained using twine and stakes to keep the entire plant over the trays to ensure trays captured shattered seed. No apparent seed predation was observed, but it is noted that this likely occurred in some areas. The trays were lined with mesh fabric using all-purpose silicone caulk so that rainwater could pass through the trays, but the seeds would be contained within the seed-collection trays. The seed-collection trays were emptied weekly using a portable vacuum, and collected seeds were placed into paper envelopes for counting. The experiments were concluded when the soybean crop reached a harvestable maturity, defined by grain moisture ranging from 13% to 15%. Target weed plants were harvested to obtain a final seed count and determine the percentage of seed retention.

Data Processing and Statistical Analysis

HWSC efficacy for a given species is dependent on the fraction of its total seed production that can be captured by the combine. The amount of seed captured is a function of the timing of crop harvest relative to crop and weed maturity, as well as weed management tactics. We anchored our analysis to the physiological maturity date of soybean at each study site because of this, and because it is a time point that growers can identify easily and use to project potential future weed maturation based on the results of this study. We focused on metrics of cumulative seed-shatter progression over the weeks following crop physiological maturity.

We calculated cumulative seed shatter as the percentage of total seed production that had dropped by a given date:

([1]) $${{\rm{S_{cum}}} = 100 \times{{{{\sum\nolimits_{{w} = 1}^t {{\rm{S}}_{{w}}} }}}\over{{\sum\nolimits_{{w} = 1}^{t\max } {{\rm{S}}_{{w}}} + {\rm{S_{ret}}}}}$$

where s cum is the cumulative percent seed shatter, w is the sampling week, t is the week through which s cum is calculated, s w is the recorded seed shatter in a given sampling week, t max is the end of the sampling season, and s ret is the unshattered seed retained at t max. We conducted three analyses based on this general calculation.

In the first analysis, we wanted to characterize broad spatial trends in seed-shatter progression of the overall weed community. Because each site chose locally dominant weeds for study, pooling the seeds at each site across species gives a generalized overview of seed-shatter phenology of common weeds at a large scale. To do this, we first calculated the cumulative percent seed shatter within each state for 2016 and 2017 by pooling the weekly seed production across species within 1 wk of soybean physiological maturity (maturity ±3 d, a 1-wk sampling window), and 2, 3, and 4 wk after soybean physiological maturity. We then plotted spatial heat maps of these values to visualize regional to continental patterns in the rates of combined broadleaf weed seed shatter during the weeks following soybean physiological maturity. States were only plotted on the map if they sampled during a given time interval. For example, if a state sampled within ±3 d of maturity (a 7-d window centered on the maturity date), we plotted it on the “week of maturity” map.

Similar calculations allowed us to identify variation in seed rain timing within and among species in our second analysis. Cumulative seed shatter was calculated for graphical analysis of each species as the percentage of seed shattered at soybean physiological maturity, and 2, 3, and 4 wk after physiological maturity by pooling across individual sampled plants at each time point within each state in 2016 and 2017. This approach gave us one value per species, per state, and per year at each weekly time interval as data allowed. These species-specific shatter rates were reclassified as categorical values corresponding with 0%< = shatter <10%, 10%< = shatter <20%, and so forth. We then calculated the percent of site-years of data that fell into each categorical bin and plotted them as heat maps to visualize the frequency distribution of seed-shatter progression week by week for each species and to compare between species.

Finally, we estimated mean per capita daily seed rain rates (i.e., seeds plant−1 day−1) and mean per capita cumulative percent seed shatter for each species during the first 1 to 4 wk following maturity, accounting for site and year differences. These metrics quantify the rate of HWSC opportunity loss for each species—an indicator of how soon growers should harvest the crop if they are hoping to control weed seeds with HWSC. To do this, we first calculated seed rain rate during the first week after physiological maturity for each sample plant as the cumulative number of seeds dropped per week after maturity minus the cumulative number of seeds dropped at physiological maturity (a week earlier) divided by the number of days elapsed between samples. We did the same for the second, third, and fourth weeks after physiological maturity by subtracting cumulative seed rain at maturity from cumulative seed rain 2, 3, or 4 wk after physiological maturity, respectively, on a per-sample basis and divided the number of days elapsed. For each species, we then fit a linear model with normally distributed errors using individual plants as the unit of replication to generate estimated marginal mean seed rain rates for each species that account for variation due to differences between sites or years. Estimates of cumulative percent seed shatter were generated by fitting generalized linear models with binomial errors (logistic regression models) to the cumulative seed-shatter data for each individual plant. Cumulative seed shatter for an individual plant at a given time point was calculated from the onset of seed shatter. These models used the same fixed-effects structures as the seed rain rate models for a given species. Estimated marginal mean values were calculated from the fitted models in the same way as the daily seed rain rate estimates. While the first two analyses quantified total seed rain pooled across individuals, these analyses included variation between individual plants, the implications of which will be explored elsewhere. Because not all species were sampled at the same sites during both 2016 and 2017, the model structures were tailored to the data available for each species. For example, species sampled in the same group of sites for both years had a balanced sampling design, so we could fit a model with site, year, and site by year interaction terms. Other species were sampled in multiple sites, but not all sites were sampled in both years. In this case, we fit an additive model with site and year effects. Still other species only allowed us to account for differences between sites or between years. These were evaluated with F-tests for seed rain rate models or χ2 likelihood ratio tests for percent seed-shatter models. For species sampled only during a single site-year, we used an intercept-only fixed-effects structure evaluated with either a t-test or χ2 test for seed rain rate or percent seed-shatter models, respectively. One species, I. hederacea, was not sampled the week of physiological maturity and could not be analyzed. All data processing and analyses were conducted in R (R Core Team 2018).

Results and Discussion

Amaranthus palmeri, A. tuberculatus, A. trifida, C. album, and Ipomoea spp. are the most common and problematic broadleaf weeds in soybean production systems in the United States (Van Wychen Reference Van Wychen2015, Reference Van Wychen2016). Within the different regions, the dominant species that co-occurred shifted with latitude. For example, A. palmeri in the South is a dominant weed that retains 95% to 100% of its seed at soybean physiological maturity (Schwartz et al. 2016). Furthermore, in this geographic area, target weeds examined retained a greater proportion of seeds from the beginning of crop physiological maturity window to 4 wk after physiological maturity compared with other areas (Figure 1). As we moved from the southern United States further north, the shatter rate increased. This result could be a function of temperature (a killing frost occurs sooner in the northern United States than in the South), weed species, and/or a cultural management strategy, such as planting date. A similar trend was seen in both 2016 and 2017.

Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a window starting from soybean physiological maturity to 4 wk past maturity in 2016 and 2017. States were included in these maps only if they conducted sampling during the week indicated (e.g., In 2017, Arkansas sampled on October 2, October 18, and November 3, none of which are within ±3 d of the October 10 maturity date or maturity +2 wk on October 24 in the state that year. Hence only data from maturity +3 wk are for Arkansas for 2017.)

Seed shatter progressed at different rates for different species, and some species had greater variation in shatter progression over space and time than others (Figures 2 and 3). Our ability to resolve this variation was limited for some species by the number of site-years sampled (Figure 2). Overall, the broadleaf species shattered less than 10% of their seeds by soybean harvest maturity at most of the sites. As time advanced, seed shatter increased for each species with the range of percent seed shatter increasing each week for all species, making timely harvest of the crop critical for success of HWSC (Table 2). Amaranthus spp. shattered a large number of seeds (17.5 to 945.7 seeds plant−1 day−1), but retained 98% to 100% of their seeds, indicating that although a large number of seeds were added to the soil seedbank, the majority of the seeds remained on the plant. Several non-amaranths also had low seed shatter: jimsonweed (Datura stramonium L.) shattered only 3.5% of its seeds at 3 wk after crop maturity and 4.5% at 4 wk, while hemp sesbania [Sesbania herbacea (Mill.) McVaugh] still retained 100% of its seeds at 4 wk after maturity (Table 2). These results mirror those that showed that many weed species at physiological maturity retain a high proportion of weed seeds (Davis Reference Davis2008; Goplen et al. Reference Goplen, Sheaffer, Becker, Coulter, Breitenbach, Behnken, Johnson and Gunsolus2016; Schwartz-Lazaro et al. Reference Schwartz-Lazaro, Norsworthy, Young, Bradley, Kruger, Davis, Steckel and Walsh2016, Reference Schwartz-Lazaro, Green and Norsworthy2017a). For example, Davis (Reference Davis2008) reported in east-central Illinois that I. hederacea retained 85% of its seed in soybean fields. In Minnesota, 80% seed retention was recorded for A. trifida at the time 75% of soybeans were already harvested in the region (Goplen et al. Reference Goplen, Sheaffer, Becker, Coulter, Breitenbach, Behnken, Johnson and Gunsolus2016), though we documented less than 40% retention in A. trifida at 4 wk (Table 2). High seed retention of >95% at soybean harvest maturity in A. palmeri and A. tuberculatus were observed in a survey conducted by Schwartz et al. (2016) across five states.

Figure 2. Cumulative percent shatter over four time periods (soybean physiological maturity, maturity + 2 wk, maturity + 3 wk, maturity + 4 wk) for each species. The darker the bar, the greater percent of sampled site-years that corresponded to the percent shatter value. This normalizes across species with different sampling efforts. Species sampled in just a single site-year are indicated by a single black square, which represents 100% of the sampling effort. Species are denoted by their EPPO codes.

Figure 3. Cumulative percent seed shatter for all species from planting date to soybean physiological maturity (black vertical line) for each state in 2016 and 2017. Species are denoted by their EPPO codes.

Table 2. Predicted per capita seed shatter (%) with standard error (SE) values at one to 4 wk after soybean physiological maturity. a

a Values are predicted across from fitted logistic regressions for each species after accounting for differences between states and years. IPOLA is not included, because it did not shatter any seeds in 2016 and was not sampled at maturity in 2017. SEBEX did not produce any seeds in 2016 in AR, and it retained 100% of its seeds during the sampling period in MS in 2016 or 2017. χ2 values are from likelihood ratio tests comparing the fitted model with a null model. No test was performed for species with just a single site-year of data (indicated as “NA”), because we had already fit intercept-only null models to these.

b XANST burs were counted, not the actual seed.

c N is equivalent to the total number of plants for all sites and years.

d Model structures were dependent on the number of sites and years for each species. The model test used in seed rain rate analyses is determined by the model structure that was fit to each species: F-tests were used for seed rain rate models with site (i.e., state) and/or year fixed effects; t-tests were used for intercept-only seed rain rate models; χ2 tests were used for likelihood ratio tests of binomial generalized linear models of seed shatter (%). No likelihood ratio tests were conducted for species with only 1 site-year of data.

Further results show the variation in the progression of seed rain for each species as well as among species (Figure 3). The most important summary of the data set is contained in the recorded cumulative seed rain values for each species in each site during the weeks following soybean physiological maturity (Table 3). Further, the specific weeds studied were grouped geographically into one of the three regions with minimal overlap. One species that varied between states from 2016 to 2017 and ranged across all regions was A. artemisiifolia. Ambrosia artemisiifolia had a large range of percent seed shatter (2% to 90%) within a given year at 30 d past soybean maturity that appeared uncorrelated with state or region. It is unknown why there was such a large span of seed retention for the species. Overall, these results indicate that some of the broadleaf species with higher rates of seed retention in the weeks following soybean physiological maturity may be good candidates for HWSC.

Table 3. Cumulative percent seed shatter of the pooled individual plants at each time interval, separated by species, state, and region.

a Regions include South-Central (SC): Arkansas (AR), Mississippi (MS), Tennessee (TN), and Texas (TX); North-Central (NC): Illinois (IL), Michigan (MI), Minnesota (MN), Missouri (MO), and Nebraska (NE); and Mid-Atlantic (MA): Delaware (DE), Maryland (MD), North Carolina (NC), Pennsylvania (PA), and Virginia (VA). A dashed line (—) indicates that there are no data for that time period.

Determining the amount of seed retention of a weed species at soybean physiological maturity through harvest is important to understand the potential inputs to the soil seedbank and to also determine which weeds would be appropriate candidates for HWSC. HWSC is a relatively new practice in the United States. While there has been research conducted on narrow windrow burning (Green Reference Green2019) and seed impact mills (both the Harrington Seed Destructor and the iHSD) (Schwartz-Lazaro et al. Reference Schwartz-Lazaro, Norsworthy, Walsh and Bagavathiannan2017b; Shergill et al. Reference Shergill, Bejleri, Davis and Mirsky2020), little research into other HWSC tactics or on large-scale use has been documented. Although this study examined a large geographic range of seed-shatter potential, it was also limited in several ways. These include: incomplete quantification of seeds lost to seed predation or seeds shattering outside the collection apparatus; unknown relevance of results to other broadleaf weeds, such as weeds with wind-dispersed seeds (e.g., Canadian horseweed [Conyza canadensis (L.) Cronquist]) that will likely escape HWSC; and weeds only growing in competition with soybean until weed inflorescenc. Additionally, how much seed reduction per species is necessary for HWSC to suppress weed populations to an economically meaningful level is unknown. Utilizing an additional integrated weed management tactic will only help preserve the effectiveness of ones that we currently have and use most often. Conversely, it is likely that HWSC use will be of most durable value when embedded within broader programs of integrated weed management, rather than used as a mainstay. In the latter scenario, HWSC may select strongly for early seed shed in weeds, and evolutionary changes in timing of seed shed are likely, given the well-established ability of weeds to rapidly evolve adaptive responses to weed management methods that become predominant selective forces in an agroecosystem (Clements et al. Reference Clements, DiTommaso, Jordan, Booth, Cardina, Doohan, Mohler, Murphy and Swanton2004).

Acknowledgments

We would like to thank the staff and students at each university for helping conduct this research, specifically Kreshnik Bejleri, Sheri Heard, John Sanders, Barbara Scott, Annie Klodd, Zach Schaefer, Russ Garetson, Vitor Damiao, Matheus Martins, Camille Werner, Bruno Flaibam, and Camila Grassmann, and each institute’s research and experimental stations. The authors would also like to thank the U.S. Department of Agriculture, Agricultural Research Service Areawide program for funding and the HATCH Program of the National Institute of Food and Agriculture and the U.S. Department of Agriculture for providing partial funding. No conflicts of interest have been declared.

Footnotes

Associate Editor: Prashant Jha, Iowa State University

References

Bitarafan, Z, Andreasen, C (2020) Harvest weed seed control: seed production and retention of Fallopia convolvulus, Sinapis arvensis, Spergula arvensis and Stellaria media at spring oat maturity. Agronomy 10:46 CrossRefGoogle Scholar
Buhler, DD, Hartzler, RG, Forcella, F (1997) Implications of weed seedbank dynamics to weed management. Weed Sci 45:329336 CrossRefGoogle Scholar
Burton, NR, Beckie, HJ, Willenborg, CJ, Shirtliffe, SJ, Schoenau, JJ, Johnson, EN (2016) Evaluating seed shatter of economically important weed species. Weed Sci 64:673682 CrossRefGoogle Scholar
Clements, DR, DiTommaso, A, Jordan, N, Booth, BD, Cardina, J, Doohan, D, Mohler, CL, Murphy, SD, Swanton, CJ (2004) Adaptability of plants invading North American cropland. Agric Ecosyst Environ 104:379398 CrossRefGoogle Scholar
Davis, AS (2008) Weed seed pools concurrent with corn and soybean harvest in Illinois. Weed Sci 56:503508 Google Scholar
Gallandt, ER (2006) How can we target the weed seedbank? Weed Sci 54:588596 CrossRefGoogle Scholar
Gill, GS, Holmes, JE (1997) Efficacy of cultural control methods for combating herbicide-resistant Lolium rigidum . Pestic Sci 51:352358 3.0.CO;2-M>CrossRefGoogle Scholar
Goplen, JJ, Sheaffer, CC, Becker, RL, Coulter, JA, Breitenbach, FR, Behnken, LM, Johnson, GA, Gunsolus, JL (2016) Giant ragweed (Ambrosia trifida) seed production and retention in soybean and field margins. Weed Technol 30:246253 CrossRefGoogle Scholar
Green, JK (2019) Use of Harvest Weed Seed Control Strategies in Arkansas Soybean. Master’s thesis. Fayetteville: University of Arkansas. https://scholarworks.uark.edu/etd/3471. Accessed: March 11, 2019Google Scholar
Haring, SC, Flessner, ML (2018) Improving soil seed bank management. Pest Manag Sci 74:24122418 CrossRefGoogle ScholarPubMed
Heap, I (2019) The International Survey of Herbicide Resistant Weeds. www.weedscience.org Accessed: March 11, 2019Google Scholar
Liebman, M, Davis, AS (2009) Managing weeds in organic farming systems: an ecological approach. Pages 173–195 in Francis C, ed. Organic Farming: The Ecological System. Madison, WI: American Society of Agronomy Google Scholar
Norsworthy, J, Walsh, M, Bagavathiannan, M, Bradley, K, Steckel, L, Kruger, G, Loux, M, Eubank, T, Davis, V, Johnson, W (2014) Harvest weed seed control: testing Australian seedbank management tactics in USA soybean. Abstract 250 in Proceedings of the Weed Science Society of America. Vancouver, BC, Canada: Weed Science Society of AmericaGoogle Scholar
Palumbi, SR (2001) Humans as the world’s greatest evolutionary force. Science 293:17861790 CrossRefGoogle ScholarPubMed
R Core Team (2018) R: A Language and Environment for Statistical Computing. Version 3.5.1. Vienna, Austria: R Foundation for Statistical Computing Google Scholar
Schwartz-Lazaro, LM, Copes, JT (2019) A review of the soil seedbank from a weed scientists’ perspective. Agronomy 9:369 CrossRefGoogle Scholar
Schwartz-Lazaro, LM, Green, JK, Norsworthy, JK (2017a) Seed retention of Palmer amaranth (Amaranthus palmeri) and barnyardgrass (Echinochloa crus-galli) in soybean. Weed Technol 31:617622 CrossRefGoogle Scholar
Schwartz-Lazaro, LM, Norsworthy, JK, Walsh, MJ, Bagavathiannan, MV (2017b) Efficacy of the integrated harrington seed destructor on weeds of soybean and rice production systems in the southern United States. Crop Sci 57:28122818 CrossRefGoogle Scholar
Schwartz-Lazaro, LM, Norsworthy, JK, Young, BG, Bradley, KW, Kruger, GR, Davis, VM, Steckel, LE, Walsh, MJ (2016) Tall waterhemp (Amaranthus tuberculatus) and Palmer amaranth (Amaranthus palmeri) seed production and retention at soybean maturity. Weed Technol 30:284290 CrossRefGoogle Scholar
Schwartz-Lazaro, LM, Shergill, LS, Evans, JA, Bagavathiannan, MV, Beam, SC, Bish, MD, Bond, JA, Bradley, KW, Curran, WS, Davis, AS, Everman, WJ, Flessner, ML, Harring, SC, Jordan, NR, Korres, NE, Lindquist, JL, Norsworthy, JK, Sanders, TL, Steckel, LE, VanGessel, MJ, Young, B, Mirsky, SB (2021) Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 2: Grass species. Weed Sci. doi: 10.1017/wsc.2020.79 Google Scholar
Shergill, LS, Bejleri, K, Davis, A, Mirsky, SB (2020) Fate of weed seeds after impact mill processing in midwestern and mid-Atlantic United States. Weed Sci 68:9297 Google Scholar
Shirtliffe, SJ, Entz, MH, Van Acker, RC (2000) Avena fatua development and seed shatter as related to thermal time. Weed Sci 48:555560 CrossRefGoogle Scholar
Taghizadeh, MS, Nicolas, ME, Cousens, RD (2012) Effects of relative emergence time and water deficit on the timing of fruit dispersal in Raphanus raphanistrum L. Crop Pasture Sci 63:10181025 CrossRefGoogle Scholar
Tidemann, BD, Hall, LM, Harker, KN, Alexander, BCS (2016) Identifying critical control points in the wild oat (Avena fatua) life cycle and the potential effects of harvest weed-seed control. Weed Sci 64:463473 CrossRefGoogle Scholar
Walsh, M, Newman, P, Powles, S (2013) Targeting weed seeds in-crop: a new weed control paradigm for global agriculture. Weed Technol 27:431436 CrossRefGoogle Scholar
Walsh, MJ, Broster, JC, Schwartz-Lazaro, LM, Norsworthy, JK, Davis, AS, Tidemann, BD, Beckie, HJ, Lyon, DJ, Soni, N, Neve, P, Bagavathiannan, MV (2018) Opportunities and challenges for harvest weed seed control in global cropping systems. Pest Manag Sci 74:22352245 CrossRefGoogle ScholarPubMed
Walsh, MJ, Harrington, RB, Powles, SB (2012) Harrington seed destructor: a new nonchemical weed control tool for global grain crops. Crop Sci 52:13431347 CrossRefGoogle Scholar
Walsh, MJ, Powles, SB (2007) Management strategies for herbicide-resistant weed populations in Australian dryland crop production systems. Weed Technol 21:332338 CrossRefGoogle Scholar
Walsh, MJ, Powles, SB (2014) High seed retention at maturity of annual weeds infesting crop fields highlights the potential for harvest weed seed control. Weed Technol 28:486493 CrossRefGoogle Scholar
Widderick, M, Keenan, M, Walsh, M (2014) Harvest weed seed control: Is there a role in northern region farming systems? Pages 153156 in Proceedings of the 19th Australasian Weeds Conference. Hobart, TAS, Australia: Council of Australasian Weed SocietiesGoogle Scholar
Van Wychen, L (2015) 2015 Baseline Survey of the Most Common and Troublesome Weeds in the United States and Canada: Weed Science Society of America National Weed Survey Dataset. http://wssa.net/wp-content/uploads/2015-weed-survey_baseline.xlsx. Accessed: February 22, 2020Google Scholar
Van Wychen, L (2016) 2016 Survey of the Most Common and Troublesome Weeds in Broadleaf Crops, Fruits & Vegetables in the United States and Canada: Weed Science Society of America National Weed Survey Dataset. http://wssa.net/wp-content/uploads/2016-weed-survey_broadleaf-crops.xlsx. Accessed: February 22, 2020Google Scholar

Altmetric attention score

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 168
Total number of PDF views: 121 *
View data table for this chart

* Views captured on Cambridge Core between 04th November 2020 - 17th April 2021. This data will be updated every 24 hours.

You have Access
Open access

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species
Available formats
×
×

Reply to: Submit a response


Your details


Conflicting interests

Do you have any conflicting interests? *