The R Package PROSPER: An Environment for Modeling Weed Population Dynamics and the Evolution of Herbicide Resistance

Weed management is a challenge for farmers worldwide, and the problem is exacerbated by the spread of weed herbicide resistance. Simulation models that combine population dynamics and genetics are valuable tools for predicting the impact of competing management options on weed density, allele frequency, and phenotypic resistance levels. The new R package PROSPER provides functions for the forward simulation of weed population dynamics on a field scale, the selection of individuals according to their sensitivity to herbicides, and the recombination of alleles during reproduction. Objects are provided to enter and save model parameters in a clear structure, and to save output data for further processing in R. The basic functions are extensible with R code. PROSPER combines individual-based population dynamics with monogenic or polygenic diploid inheritance and flexible selection pressure. Stochasticity can be included at all model steps. Two examples of the population dynamics of two annual weed species with herbicide resistance are presented. All parameters and the models are available in PROSPER. In addition to simulation, PROSPER is intended for sharing and publishing population dynamic parameters and models, which is easily done thanks to R.

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Defining Integrated Weed Management: A Novel Conceptual Framework for Models

Agronomy ◽

10.3390/agronomy11040747 ◽

2021 ◽

Vol 11 (4) ◽

pp. 747

Author(s):

Jonathan Storkey ◽

Joseph Helps ◽

Richard Hull ◽

Alice E. Milne ◽

Helen Metcalfe

Keyword(s):

Population Dynamics ◽

Conceptual Framework ◽

Herbicide Resistance ◽

Weed Management ◽

Integrated Weed Management ◽

Management Options ◽

Weed Population ◽

Weed Population Dynamics ◽

Population Dynamics Models ◽

Dynamics Models

Weed population dynamics models are an important tool for predicting the outcome of alternative Integrated Weed Management (IWM) scenarios. The growing problem of herbicide resistance has increased the urgency for these tools in the design of sustainable IWM solutions. We developed a conceptual framework for defining IWM as a standardised input template to allow output from different models to be compared and to design IWM scenarios. The framework could also be used as a quantitative metric to determine whether more diverse systems are more sustainable and less vulnerable to herbicide resistance using empirical data. Using the logic of object-oriented programming, we defined four classes of weed management options based on the stage in the weed life cycle that they impact and processes that mediate their effects. Objects in the same class share a common set of properties that determine their behaviour in weed population dynamics models. Any weed control “event” in a system is associated with an object, meaning alternative management scenarios can be built by systematically adding events to a model either to compare existing systems or design novel approaches. Our framework is designed to be generic, allowing IWM systems from different cropping systems and countries to be compared.

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Noxious Weed Population Dynamics Education Model

Weed Technology ◽

10.1017/s0890037x00041555 ◽

1997 ◽

Vol 11 (1) ◽

pp. 182-188 ◽

Cited By ~ 2

Author(s):

Bruce D. Maxwell ◽

Roger L. Sheley

Keyword(s):

Population Dynamics ◽

Weed Management ◽

Educational Software ◽

Management Options ◽

Community Interactions ◽

Weed Population ◽

Weed Population Dynamics ◽

Demographic Processes ◽

Noxious Weed ◽

Population Demographic

An educational software program was developed to demonstrate the interactions among weed population demographic processes, plant community interactions, and grassland noxious weed management options. The software includes an example model parameterized to simulate yellow starthistle population dynamics and graphic output. The software allows students to change weed and competing grass population demographic parameters, herbicide, and biological control management options. The graphic output allows the user to observe the outcome of integration of weed biology and various weed management options.

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