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Macropartisanship is a measure of aggregate trends in party identification in the mass public that allows researchers to track partisanship dynamically. In previous research, macropartisanship was found to vary in concert with major political events and forces like presidential approval and the economy. However, studying macropartisanship as an aggregate trend assumes that group dynamics within the measure are equivalent. We present a series of new measures of macropartisanship using Stimson’s (2018) dyad ratio approach disaggregated by race and ethnicity. We detail the creation of measures for White, Latino, and Black macropartisanship from 1983 to 2016 using more than 500 surveys from CBS News and CBS/New York Times. The resulting data collection is publicly available and can be downloaded in monthly, quarterly, or yearly format. Our initial analysis of these data show that thinking about macropartisanship as a single aggregate measure masks important and significant variation in our understanding of party identification. Change in the measures are uncorrelated. Latino macropartisanship is more volatile and responds more to economic conditions, Black macropartisanship is very stable and has become more Democratic in response to increased polarization, while White macropartisanship has become less responsive to economic conditions as has become more Republican as Republicans have moved to the right.
Intensive field surveys were conducted in eastern Nebraska to determine the frequency distribution model and associated parameters of broadleaf and grass weed seedling populations. The negative binomial distribution consistently fit the data over time (1992 to 1993) and space (fields) for both the inter and intrarow broadleaf and grass weed seedling populations. The other distributions tested (Poisson with zeros, Neyman type A, logarithmic with zeros, and Poisson-binomial) did not fit the data as consistently as the negative binomial distribution. Associated with the negative binomial distribution is a k parameter. k is a nonspatial aggregation parameter related to the variance at a given mean value. The k parameter of the negative binomial distribution was consistent across weed density for individual weed species in a given field except for foxtail spp. populations. Stability of the k parameter across field sites was assessed using the likelihood ratio test There was no stable or common k value across field sites and years for all weed species populations. The lack of stability in k across field sites is of concern, because this parameter is used extensively in the development of parametric sequential sampling procedures. Because k is not stable across field sites, k must be estimated at the time of sampling. Understanding the variability in it is critical to the development of parametric sequential sampling strategies and understanding the dynamics of weed species in the field.
Intensive surveys were conducted in 2 fields in eastern Nebraska to determine the spatial stability of common sunflower, velvetleaf, green and yellow foxtail, and hemp dogbane over 4 yr (1992 to 1995). The 1st field was planted to soybean in 1992 and corn in 1993, 1994, and 1995. The 2nd field was planted to corn in 1992 and 1994 and soybean in 1993 and 1995. Weed density was sampled prior to postemergence herbicide application at approximately 800 locations per year in each field on a regular 7 m grid. The same locations were sampled every year. Weed density at locations between the sample sites was determined by linear triangulation interpolation. Weed seedling distribution was significantly aggregated, with large weed-free areas in both fields. Common sunflower, velvetleaf, and hemp dogbane patches were very persistent in diameter in the east-west and north-south directions and in location and area over 4 yr in the 1st field. Foxtail distribution and density continuously increased in each of the 4 yr in the first field and decreased in the 2nd field. A geographic information system was used to overlay maps from each year for a species. This showed that 36% of the sampled area was continuously free of common sunflower, 62.5% was free of hemp dogbane, and 11.5% was free of velvetleaf in the 1st field, but only 1% was free of velvetleaf in the 2nd field. The persistence of broadleaf weed patches suggests that weed seedling distributions mapped in one year are good predictors of future seedling distributions. Improved and more efficient sampling methods are needed.
An intensive survey of two farmer-managed corn and soybean fields in eastern Nebraska was conducted to investigate parametric sequential sampling of weed seedling populations using a multistage procedure to estimate k, of the negative binomial distribution. k is a nonspatial aggregation parameter related to the variance at a given mean value. Mean weed seedling density ranged from 0.18 to 3.11 plants 0.38 m−2 (linear meter of crop row) based on 806 sampling locations. The average value of k, derived from 200 multistage estimation procedures, ranged from 0.17 to 0.32. A sequential sampling plan was developed with the goal of estimating the mean with a coefficient of variation (CV) of 10, 20, 30, and 40% of the sample mean. A sampling plan was also constructed to estimate the mean within a specified distance H of the true mean (H(x̄)= 0.10, 0.50 and 1.0 plants 0.38 m−2) with 80, 85, and 90% confidence. Estimating mean weed seedling density within a specified CV of the true mean CV(x̄) using parametric sequential sampling techniques was superior to estimating the mean within a specified distance (H(x̄)) of the true mean when considering the frequency of sampling and probability of error, especially at intermediate k values. At a k: value of 0.32 and 0.25, the difference between the actual CV(x̄) obtained from sampling and the CV(x̄) specified by the sampler was minimal. However, the accuracy of weed seedling density estimates was reduced with decreasing k values below 0.25, especially as the specified CV(x̄) increased.
Seed dispersal, interacting with environmental disturbance and management across heterogeneous landscapes, results in irregular weed spatial distributions. Describing, predicting, and managing weed populations requires an understanding of how weeds are distributed spatially and the consequences of this distribution for population processes. Semivariograms and kriged maps of weed populations in several fields have helped describe spatial structure, but few generalizations can be drawn except that populations are aggregated at one or more scales. Limited information is available on the effect of weed arrangement, pattern, or field location on weed population processes. Because weeds are neither regular nor uniform in distribution, mean density alone is of limited value in estimating yield loss or describing population dynamics over a whole field. Sampling strategies that account for spatial distribution can increase sampling efficiency. Further research should focus on understanding processes that cause changes in spatial distributions over time to help predict rates of invasion and potential extent of colonization.
Field experiments were conducted in central Missouri in 1989 and 1990 to evaluate weed control practices in conjunction with cover crops and cover management systems in reduced tillage corn. There was no difference in weed control among soybean stubble, hairy vetch, and rye soil cover when averaged over cover management systems and herbicide treatments. However, mowed hairy vetch and rye covers provided greater weed control in the no-till plots than soybean stubble when no herbicide was used. Differences in weed control among cover management systems were reduced or eliminated when a PRE herbicide was applied. corn population and height were reduced by hairy vetch and rye soil cover. Corn grain yield was reduced in rye plots both years. There was no difference in grain yield between tilled and no-till plots.
Weed management can be a significant challenge in cropping systems, partly because the effects of tillage systems on weed seedbank and seedling population dynamics are not well understood. Field research was conducted from 1994 to 1996 in established tillage plots consisting of moldboard plow (MP), chisel plow (CP), and no-tillage (NT). The objectives were to determine the effects of long-term tillage systems on the timing and duration of Setaria spp. emergence and percentage cumulative emergence from the soil seedbank and to investigate the effect of tillage on Setaria spp. density and seed production following glyphosate application at Setaria spp. heights of 5, 10, and 15 cm. NT contained a greater number of Setaria spp. seed in the 0- to 1-, 1- to 3-, and 3- to 6-cm depths than MP or CP systems. There was little difference between the three tillage systems at depths greater than 6 cm. Setaria spp. emergence was greater in NT than in MP or CP in 1994 and 1996 and greater than in MP in 1995. There was a substantial increase in Setaria spp. emergence in NT between 3 and 4 weeks after planting (WAP) in 1994 and between 5 and 6 WAP in 1995 and 1996. Significant emergence did not occur past 5 to 6 WAP in 1994 and 1995 but continued over a longer period of time in 1996. Setaria spp. plants consistently reached targeted herbicide application heights 4 to 9 d earlier in NT than in CP and MP. In 1994, final Setaria spp. density was greater in NT compared to CP and MP at the 5- and 10-cm herbicide application timings. When glyphosate was applied to 15-cm-tall Setaria, very few weeds were present following application across all tillage systems. In 1995, NT resulted in greater Setaria spp. density than MP or CP across all application timings. There was no difference in final Setaria spp. density between MP and CP across all glyphosate timings in 1994 and 1995. Seed production was negligible in MP and CP, regardless of glyphosate timing. In NT, however, significant seed production occurred, especially with early application. Results indicate that the effectiveness of nonresidual herbicides for Setaria faberi Herrm. control is influenced by tillage system and the timing of application.
An intensive field survey of an eastern Nebraska corn and soybean field was conducted to characterize the spatial structure and temporal stability of broadleaf weed seedling populations over two growing seasons. Anisotropy, the effect of direction on the relationship between observations, is present in the semivariogram for the velvetleaf and common sunflower populations in 1992 and 1993. The directional trends in aggregation are visible in kriged maps as elliptical shapes oriented east to west across the study area. In addition, there are two distinct zones of aggregation from north to south. These two distinct areas of aggregation are reflected as a ‘plateau’ in the north-south semivariogram. The distance over which this plateau extends indicates that the shape or size of the patch is contracting in the north-south direction (perpendicular to the crop row). The slope of the semivariogram in the east-west direction (aligned with the crop row) remains consistent from 1992 to 1993 suggesting that the shape of the patch is not changing. For sunflower populations, the slope of the north-south empirical semivariogram changes at 20 m, similar to the velvetleaf population semivariograms. This change, however, is reflected as a downward trend in the empirical semivariogram. The distance over which this trend occurs increases from 1992 to 1993 suggesting that seedling patch size was smaller in 1993 compared to 1992. Weed seedling establishment resulting from seed dispersal, differential seed and seedling mortality, or emergence may have resulted in the observed patch dynamics.
Three methods of predicting the impact of weed interference on crop yield and expected economic return were compared to evaluate the economic importance of weed spatial heterogeneity. Density of three weed species was obtained using a grid sampling scheme in 11 corn and 11 soybean fields. Crop yield loss was predicted assuming densities were homogeneous, aggregated following a negative binomial with known population mean and k, or aggregated with weed densities spatially mapped. Predicted crop loss was lowest and expected returns highest when spatial location of weed density was utilized to decide whether control was justified. Location-specific weed management resulted in economic gain as well as a reduction in the quantity of herbicide applied.
Weed seedbanks have been studied intensively at local scales, but to date, there have been no regional-scale studies of weed seedbank persistence. Empirical and modeling studies indicate that reducing weed seedbank persistence can play an important role in integrated weed management. Annual seedbank persistence of 13 summer annual weed species was studied from 2001 through 2003 at eight locations in the north central United States and one location in the northwestern United States. Effects of seed depth placement, tillage, and abiotic environmental factors on seedbank persistence were examined through regression and multivariate ordinations. All species examined showed a negative relationship between hydrothermal time and seedbank persistence. Seedbank persistence was very similar between the two years of the study for common lambsquarters, giant foxtail, and velvetleaf when data were pooled over location, depth, and tillage. Seedbank persistence of common lambsquarters, giant foxtail, and velvetleaf from October 2001 through 2002 and October 2002 through 2003 was, respectively, 52.3% and 60.0%, 21.3% and 21.8%, and 57.5% and 57.2%. These results demonstrate that robust estimates of seedbank persistence are possible when many observations are averaged over numerous locations. Future studies are needed to develop methods of reducing seedbank persistence, especially for weed species with particularly long-lived seeds.
WeedSOFT is a state-of-the-art decision support system for weed management in the north central region of the United States, but its accuracy to predict corn yield loss associated with later-emerging weed communities has not been adequately assessed. We conducted experiments in 2004 and 2005 to compare observed and predicted corn yield related to four establishment times of mixed-species weed communities for validation of competitive index modifier (CIM) values in WeedSOFT. Weed communities were established at VE, V2, V4, and V6 corn (emergence, second-leaf, fourth-leaf, and sixth-leaf stages, respectively), and consisted largely of annual grass and moderately competitive annual broadleaf species. Compared to weed-free corn, yield loss occurred in each of seven site-years for weed communities established at VE corn, but in only one site-year for communities established at V2 corn. No corn yield loss was associated with weed communities established at V4 or V6 corn. For communities established at VE corn, predicted corn yield differed from observed yield in all but one site year, with predicted yield less than observed yield in three site-years, and greater than observed yield in two site-years; however, nonlinear regression analyses of yield data pooled over site-years showed that fitted values were similar between predicted and observed yield. For communities established at V2 and V4 corn, predicted yield was less than observed yield in six and five site-years, respectively. For communities established at V6 corn, predicted yield was less than observed yield in three of six site-years, but was similar to observed yield in three of six site-years. These results indicated that the CIM values in WeedSOFT tended to overestimate the competitiveness of late-emerging weed communities. Corn yield data from a pooled analysis of all site-years were used to generate a revised set of growth stage CIM values, which improved the accuracy of predicted corn yield. These results should improve weed management decisions and reduce the need for herbicide applications to late-emerging weeds.
Potential crop yield loss due to early-season weed competition is an important risk associated with postemergence weed management programs. WeedSOFT is a weed management decision support system that has the potential to greatly reduce such risk. Previous research has shown that weed emergence time can greatly affect the accuracy of corn yield loss predictions by WeedSOFT, but our understanding of its predictive accuracy for soybean yield loss as affected by weed emergence time is limited. We conducted experiments at several sites across the Midwestern United States to assess accuracy of WeedSOFT predictions of soybean yield loss associated with mixed-species weed communities established at emergence (VE), cotyledon (VC), first-node (V1), or third-node (V3) soybean. Weed communities across research sites consisted mostly of annual grass species and moderately competitive annual broadleaf species. Soybean yield loss occurred in seven of nine site-years for weed communities established at VE soybean, four site-years for weed communities established at VC soybean, and one site-year for weed communities established at V1 soybean. No soybean yield loss was associated with weed communities established at the V3 stage. Nonlinear regression analyses of predicted and observed soybean yield data pooled over site-years showed that predicted yields were less than observed yields at all soybean growth stages, indicating overestimation of soybean yield loss. Pearson correlation analyses indicated that yield loss functions overestimated the competitive ability of high densities of giant and yellow foxtail with soybean, indicating that adjustments to competitive index values or yield loss function parameters for these species may improve soybean yield loss prediction accuracy and increase the usefulness of WeedSOFT as a weed management decision support system.
Field experiments were conducted from 1997 to 1999 at the University of Minnesota Southern Research and Outreach Center in Waseca to evaluate the (1) effect of corn row spacing on grass and broadleaf weed species density and height, (2) optimal herbicide application timing in narrow- and wide-row systems, and (3) corn grain yield response to row spacing and herbicide application timing. Corn was planted in 51- and 76-cm row spacings. Within each row-spacing treatment, there were five herbicide application timings: a formulated mixture of acetochlor plus atrazine applied preemergence or a formulated mixture of imazethapyr and imazapyr tank-mixed with bromoxynil applied postemergence at 5-, 10-, 20-, or 30-cm giant foxtail plant height. Reducing the row spacing in corn from 76 to 51 cm did not influence early-season weed emergence or growth. Similarly, late-season weed density and growth were not influenced by row spacing except in 1997. But corn grain yield increased when corn was planted in narrow rows compared with wide rows in 2 out of 3 yr when averaged over herbicide application treatments. Herbicide application timing had a significant effect on late-season weed density and grain yield. But there was no interaction between herbicide application timing and row spacing on grain yield. Potential increases in crop competitiveness resulting from narrow-row corn did not appear to affect weed density or growth in this study.
Herbicide evaluation trials are typically conducted with the objective of rating herbicide efficacy and assessing crop yield loss. There is little if any attempt to quantify the economic risk associated with each treatment. The objective of this research was to use second-degree stochastic dominance to evaluate the economic stability of corn and soybean weed management systems between two contrasting environments. Weed management systems were evaluated in small-plot replicated trials over a 3-yr time period at two locations in southern Minnesota. One location (Waseca) had a slightly cooler and wetter environment than the second location (Lamberton). The Waseca location also had higher weed density and greater weed species diversity. Adjusted returns from weed management were calculated for each system by measuring economic returns, as determined by deducting weed management costs from the product of crop price and grain yield. Stochastic dominance is a technique that considers the entire distribution of net returns from weed management and compares these cumulative distributions as a basis for analyzing risk. Climate, soils, and weed diversity dictated differences in risk efficiency and effectiveness of the various weed management systems evaluated between the Waseca and Lamberton sites. Stochastic dominance testing is a useful tool for understanding long-term risk across environments. Results can be used to develop effective long-term weed management systems that minimize risk while maximizing profit potential.
Variation in crop–weed interference relationships has been shown for a number of crop–weed mixtures and may have an important influence on weed management decision-making. Field experiments were conducted at seven locations over 2 yr to evaluate variation in common lambsquarters interference in field corn and whether a single set of model parameters could be used to estimate corn grain yield loss throughout the northcentral United States. Two coefficients (I and A) of a rectangular hyperbola were estimated for each data set using nonlinear regression analysis. The I coefficient represents corn yield loss as weed density approaches zero, and A represents maximum percent yield loss. Estimates of both coefficients varied between years at Wisconsin, and I varied between years at Michigan. When locations with similar sample variances were combined, estimates of both I and A varied. Common lambsquarters interference caused the greatest corn yield reduction in Michigan (100%) and had the least effect in Minnesota, Nebraska, and Indiana (0% yield loss). Variation in I and A parameters resulted in variation in estimates of a single-year economic threshold (0.32 to 4.17 plants m−1 of row). Results of this study fail to support the use of a common yield loss–weed density function for all locations.
There are significant concerns over the long- and short-term implications of continuous glyphosate use and potential problems associated with weed species shifts and the development of glyphosate-resistant weed species. Field research was conducted to determine the effect of herbicide treatment and application timing on weed control in glyphosate-resistant soybean. Ten herbicide treatments were evaluated that represented a range of PPI, PRE, and POST-only application timings. All herbicide treatments included a reduced rate of glyphosate applied POST. PRE herbicides with residual properties followed by (fb) glyphosate POST provides more effective control of broadleaf weed species than POST-only treatments. There was no difference in soybean yield between PRE fb POST and POST-only treatments in 2008. Conversely, PRE fb POST herbicide treatments resulted in greater yield than POST-only treatments in 2009. Using PRE fb POST herbicide tactics improves weed control and reduces the risk for crop yield loss when dealing with both early- and late-emerging annual broadleaf weed species across variable cropping environments.
The size, location, and variation in time of weed patches within an arable field were analyzed with the ultimate goal of simplifying weed mapping. Annual and perennial weeds were sampled yearly from 1993 to 1997 at 410 permanent grid points in a 1.3-ha no-till field sown to row crops each year. Geostatistical techniques were used to examine the data as follows: (1) spatial structure within years; (2) relationships of spatial structure to literature-derived population parameters, such as seed production and seed longevity; and (3) stability of weed patches across years. Within years, densities were more variable across crop rows and patches were elongated along rows. Aggregation of seedlings into patches was strongest for annuals and, more generally, for species whose seeds were dispersed by combine harvesting. Patches were most persistent for perennials and, more generally, for species whose seeds dispersed prior to expected dates of combine harvesting. For the most abundant weed in the field, the annual, Setaria viridis, locations of patches in the current year could be used to predict patch locations in the following year, but not thereafter.
As herbicide-resistant weed populations become increasingly problematic in crop production, alternative strategies of weed control are necessary. Giant ragweed, one of the most competitive agricultural weeds in row crops, has evolved resistance to multiple herbicide biochemical sites of action within the plant, necessitating the development of new and integrated methods of weed control. This study assessed the quantity and duration of seed retention of giant ragweed grown in soybean fields and adjacent field margins. Seed retention of giant ragweed was monitored weekly during the 2012 to 2014 harvest seasons using seed collection traps. Giant ragweed plants produced an average of 1,818 seeds per plant, with 66% being potentially viable. Giant ragweed on average began shattering hard (potentially viable) and soft (nonviable) seeds September 12 and continued through October at an average rate of 0.75 and 0.44% of total seeds per day during September and October, respectively. Giant ragweed seeds remained on the plants well into the Minnesota soybean harvest season, with an average of 80% of the total seeds being retained on October 11, when Minnesota soybean harvest was approximately 75% completed in the years of the study. These results suggest that there is a sufficient amount of time to remove escaped giant ragweed from production fields and field margins before the seeds shatter by managing weed seed dispersal before or at crop harvest. Controlling weed seed dispersal has potential to manage herbicide-resistant giant ragweed by limiting replenishment of the weed seed bank.
Evaluation of economic outcome associated with a given weed management system is an important component in the decision-making process within crop production systems. The objective of this research was to investigate how risk-efficiency criteria could be used to improve herbicide-based weed management decision making, assuming different risk preferences among growers. Data were obtained from existing weed management trials in corn conducted at the University of Minnesota Southern Research and Outreach Center at Waseca. Weed control treatments represented a range of practices including one-pass soil-applied, one-pass postemergence, and sequential combinations of soil and postemergence herbicide application systems. Analysis of risk efficiency across 23 herbicide-based weed control treatments was determined with the mean variance and stochastic dominance techniques. We show how these techniques can result in different outcomes for the decision maker, depending on risk attitudes. For example, mean variance and stochastic dominance techniques are used to evaluate risk associated with one- vs. two-pass herbicide treatments with and without cultivation. Based on these analyses, it appears that a one-pass system is preferred by a risk-averse grower. However, we argue that this may not be the best option considering potential changes in weed emergence patterns, application timing concerns, etc. The techniques for economic analysis of weed control data outlined in this article will help growers match herbicide-based weed management systems to their own production philosophies based on economic risk.