Horseweed (Marestail) Resistance

Horseweed has documented resistance to EPSPS inhibitors, PSII inhibitors, ALS inhibitors, and PSI inhibitor (Heap, 2018). Glyphosate resistant horseweed moves the herbicide into a vacuole preventing the herbicide from damaging the plant (Ge, 2010). The objective of the this experiment was to determine if nine different horseweed populations were resistant or not to six different herbicide. Experimental design was a randomized complete block with six herbicides, nine locations, and five replications. Heights were recorded for each population before spraying. Each herbicide was sprayed on October 5, 2018, and rates were paraquat (840 g ai/ha), glyphosate (1260 g ae/ha), glufosinate (738 g ai/ha), Atrazine (560 g ai/ha), chlorimuron (13.1 g ai/ha), and dicamba (560 g ai/ha). Horseweed showed the most resistance to atrazine and glyphosate across all locations, and paraquat in some locations. No herbicide had total control fourteen days after spraying. Dicamba had the greatest control of horseweed across all nine locations. Resistance was difficult to identify because horseweed plants were too mature to effectively be control

Viable Shattercane Seed

a. Shattercane is a common weed that many farmers all over Kansas have to deal with. This project should help farmers get a better idea when those shattercane plants become able to reproduce.!b. When does shattercane heads become a potential problem to produce viable seeds?!c.The control group of fully mature shattercane seeds where the only seeds that sprouted in the 5 day test period. I found that more time may be necessary for the less mature seeds to sprout. !d. The results from this testing can be important to farmers to know when they need to take action to decrease risks of shattercane problems in the future

Fitness Outcomes Related to Glyphosate Resistance in Kochia (Kochia scoparia): What Life History Stage to Examine?

A fast-spreading weed, kochia (Kochia scoparia), has developed resistance to the widely-used herbicide, glyphosate. Understanding the relationship between the occurrence of glyphosate resistance caused by multiple EPSPS gene copies and kochia fitness may suggest a more effective way of controlling kochia. A study was conducted to assess fitness cost of glyphosate resistance compared to susceptibility in kochia populations at different life history stages, that is rate of seed germination, increase in plant height, days to flowering, biomass accumulation at maturity, and fecundity. Six kochia populations from Scott, Finney, Thomas, Phillips, Wallace, and Wichita counties in western Kansas were characterized for resistance to field-use rate of glyphosate and with an in vivo shikimate accumulation assay. Seed germination was determined in growth chambers at three constant temperatures (5, 10, and 15 C) while vegetative growth and fecundity responses were evaluated in a field study using a target-neighborhood competition design in 2014 and 2015. One target plant from each of the six kochia populations was surrounded by neighboring kochia densities equivalent to 10 (low), 35 (moderate), or 70 (high) kochia plants m−2. In 2015, neighboring corn densities equivalent to 10 and 35 plants m−2 were also evaluated. Treatments were arranged in a randomized complete block design with at least 7 replications. Three kochia populations were classified as glyphosate-resistant (GR) [Scott (SC-R), Finney (FN-R), and Thomas (TH-R)] and three populations were classified as glyphosate-susceptible (GS) [Phillips (PH-S), Wallace (WA-S) and Wichita (WI-S)]. Of the life history stages measured, fitness differences between the GR and GS kochia populations were consistently found in their germination characteristics. The GR kochia showed reduced seed longevity, slower germination rate, and less total germination than the GS kochia. In the field, increases in plant height, biomass accumulation, and fecundity were not clearly different between GR and GS kochia populations (irrespective of neighbor density). Hence, weed management plans should integrate practices that take advantage of the relatively poor germination characteristics of GR kochia. This study suggests that evaluating plant fitness at different life history stages can increase the potential of detecting fitness costs

Common Sunflower Seedling Emergence across the U.S. Midwest

Predictions of weed emergence can be used by practitioners to schedule POST weed management operations. Common sunflower seed from Kansas was used at six Midwestern U.S. sites to examine the variability that 16 climates had on common sunflower emergence. Nonlinear mixed effects models, using a flexible sigmoidal Weibull function that included thermal time, hydrothermal time, and a modified hydrothermal time (with accumulation starting from January 1 of each year), were developed to describe the emergence data. An iterative method was used to select an optimal base temperature (Tb) and base and ceiling soil matric potentials (ψb and ψc) that resulted in a best-fit regional model. The most parsimonious model, based on Akaike\u27s information criterion (AIC), resulted when Tb = 4.4 C, and ψb = −20000 kPa. Deviations among model fits for individual site years indicated a negative relationship (r = −0.75; P \u3c 0.001) between the duration of seedling emergence and growing degree days (Tb = 10 C) from October (fall planting) to March. Thus, seeds exposed to warmer conditions from fall burial to spring emergence had longer emergence periods

Local Conditions, Not Regional Gradients, Drive Demographic Variation of Giant Ragweed (Ambrosia trifida) and Common Sunflower (Helianthus annuus) Across Northern U.S. Maize Belt

Knowledge of environmental factors influencing demography of weed species will improve understanding of current and future weed invasions. The objective of this study was to quantify regional-scale variation in vital rates of giant ragweed and common sunflower. To accomplish this objective, a common field experiment was conducted across seven sites between 2006 and 2008 throughout the north central U.S. maize belt. Demographic parameters of both weed species were measured in intra- and interspecific competitive environments, and environmental data were collected within site-years. Site was the strongest predictor of belowground vital rates (summer and winter seed survival and seedling recruitment), indicating sensitivity to local abiotic conditions. However, biotic factors influenced aboveground vital rates (seedling survival and fecundity). Partial least squares regression (PLSR) indicated that demography of both species was most strongly influenced by thermal time and precipitation. The first PLSR components, both characterized by thermal time, explained 63.2% and 77.0% of variation in the demography of giant ragweed and common sunflower, respectively; the second PLSR components, both characterized by precipitation, explained 18.3% and 8.5% of variation, respectively. The influence of temperature and precipitation is important in understanding the population dynamics and potential distribution of these species in response to climate change

An Affinity–Effect Relationship for Microbial Communities in Plant–Soil Feedback Loops

Feedback loops involving soil microorganisms can regulate plant populations. Here, we hypothesize that microorganisms are most likely to play a role in plant–soil feedback loops when they possess an affinity for a particular plant and the capacity to consistently affect the growth of that plant for good or ill. We characterized microbial communities using whole-community DNA fingerprinting from multiple "home-and-away" experiments involving giant ragweed (Ambrosia trifida L.) and common sunflower (Helianthus annuus L.), and we looked for affinity–effect relationships in these microbial communities. Using canonical ordination and partial least squares regression, we developed indices expressing each microorganism's affinity for ragweed or sunflower and its putative effect on plant biomass, and we used linear regression to analyze the relationship between microbial affinity and effect. Significant linear affinity–effect relationships were found in 75% of cases. Affinity–effect relationships were stronger for ragweed than for sunflower, and ragweed affinity–effect relationships showed consistent potential for negative feedback loops. The ragweed feedback relationships indicated the potential involvement of multiple microbial taxa, resulting in strong, consistent affinity–effect relationships in spite of large-scale microbial variability between trials. In contrast, sunflower plant–soil feedback may involve just a few key players, making it more sensitive to underlying microbial variation. We propose that affinity–effect relationship can be used to determine key microbial players in plant–soil feedback against a low "signal-to-noise" background of complex microbial datasets

Appearance of Herbicide Resistance in a Weed Population

Introduction Overview: Through the repeated use of the same herbicide, weed populations can consist of susceptible (S)-biotypes that are controlled and herbicide resistant (R)-biotypes that are left behind to produce and return seed with the resistance characteristic back into the soil. This lesson will highlight the population dynamics of a mixed weed population, containing S- and R-biotypes, and compare and contrast the rate at which herbicide resistant weeds appear in a population under a diversity of selection pressures. This lesson will highlight the population dynamics of a mixed (herbicide susceptible and resistant biotype) weed population, and compare and contrast the rate of appearance of herbicide resistance in a mixed population under a diversity of selection pressures. Objectives: At the completion of this lesson, students will: 1. Understand the differences between susceptible, tolerant, and resistant weed populations. 2. Describe diagrammatically and mathematically, the dynamics of an annual weed species population composed of S- and R-biotypes. 3. Describe herbicide characteristics, aspects of weed species biology, and management practices that alter intensity of selection pressure and impact the rate at which herbicide resistance appears in a weed population. 4. Devise a weed management plan with a diversity of selection pressures to reduce the rate at which herbicide resistance appears. Modules: Lesson home Introduction What is Herbicide Resistance? What is Population Dynamics? Rate at Which Herbicide Resistance Appears in Weed Populations How to Manage Herbicide Resistant Weeds Summary References Glossary Video

Aparición de resistencia a herbicidas, en una población de malezas

Introducción Resumen: A través del uso repetido del mismo herbicida, los biotipos susceptibles (S)- de una especie de maleza son controlados, mientras que los biotipos resistentes (R)- al herbicida escapan para producir y retornar semilla con la característica de resistencia al suelo. Esta lección resaltará la dinámica poblacional de una población de malezas mezclada (biotipos S y R); comparará y contrastará la tasa a la cual aparece resistencia a un herbicida en una polación de malezas sometida a varias presiones de selección. El uso repetido del mismo herbicida, puede provocar poblaciones de malezas que consisten de biotipos susceptibles (S) que son controlados y biotipos resistentes (R), que escapan al control para producir y retornar semilla con la característica de resistencia, al banco de semillas del suelo. Esta lección se enfocará en la dinámica poblacional de una población de malezas mezclada con biotipos S y R. Se comparará y contrastará la tasa a la que aparecen malezas resistentes en una población bajo diversas presiones de selección . ****** Esta lección se enfocará en la dinámica poblacional de una población mezclada (biotipos susceptibles y resistentes a un herbicida), y comparar y contrastar la tasa a la cual aparece resistencia al herbicida, en una población de malezas mezclada, bajo diversas presiones de selección. Objetivos: Al completar esta lección, los estudiantes: 1. Entenderán las diferencias entre poblaciones de alezas susceptibles, tolerantes, y resistentes. 2. Describirán diagramática y matemáticamente, la dinámica poblacional de una especie de maleza anual, compuesta de biotipos S y R. 3. Describirán características del herbicida, aspectos de la biología de la maleza, y prácticas de manejo que alteran la intensidad de la presión de selección e impactan la tasa a la cual aparece resistencia al herbicida, en una población de malezas. 4. Idearán un plan de manejo de la maleza con varias presiones de selección, para reducir la tasa de aparición de resistencia al herbicida. Modules: Lesson home Introducción Qué es resistencia a herbicidas? Qué es dinámica poblacional? Tasa de aparición de resistencia a herbicidas, en poblaciones de malezas Cómo manejar malezas resistentes a herbicidas? Resumen Bibliografía Glossary Video

Appearance of Herbicide Resistance in a Weed Population

Introduction Overview: Through the repeated use of the same herbicide, weed populations can consist of susceptible (S)-biotypes that are controlled and herbicide resistant (R)-biotypes that are left behind to produce and return seed with the resistance characteristic back into the soil. This lesson will highlight the population dynamics of a mixed weed population, containing S- and R-biotypes, and compare and contrast the rate at which herbicide resistant weeds appear in a population under a diversity of selection pressures. This lesson will highlight the population dynamics of a mixed (herbicide susceptible and resistant biotype) weed population, and compare and contrast the rate of appearance of herbicide resistance in a mixed population under a diversity of selection pressures. Objectives: At the completion of this lesson, students will: 1. Understand the differences between susceptible, tolerant, and resistant weed populations. 2. Describe diagrammatically and mathematically, the dynamics of an annual weed species population composed of S- and R-biotypes. 3. Describe herbicide characteristics, aspects of weed species biology, and management practices that alter intensity of selection pressure and impact the rate at which herbicide resistance appears in a weed population. 4. Devise a weed management plan with a diversity of selection pressures to reduce the rate at which herbicide resistance appears. Modules: Lesson home Introduction What is Herbicide Resistance? What is Population Dynamics? Rate at Which Herbicide Resistance Appears in Weed Populations How to Manage Herbicide Resistant Weeds Summary References Glossary Video