Special Issue "The Application of Models for Weed Management in Cropping Systems"

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Weed Science and Weed Management".

Deadline for manuscript submissions: 20 February 2021.

Special Issue Editors

Dr. Helen Metcalfe
Website
Guest Editor
Sustainable Agricultural Sciences, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK
Interests: weed ecology; agricultural systems management; modelling
Dr. Jon Storkey
Website
Guest Editor
Sustainable Agricultural Sciences, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK
Interests: weed ecology; agricultural systems management; modelling
Dr. Alice E. Milne
Website
Guest Editor
Sustainable Agricultural Sciences, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK
Interests: weed ecology; agricultural systems management; modelling

Special Issue Information

Dear Colleagues,

Weeds are thought to have the potential to reduce global food production by 34%. In the developed world, farmers largely depend on the application of herbicides to control their weeds; however, this important method of control is being eroded. This is because: (1) increasingly tighter restrictions are being imposed on the use of herbicides; and (2) weeds are evolving a resistance to many of the available actives. It is, therefore, more important than ever to develop new effective methods for weed-control that slow the evolution of resistance and avoid the negative environmental impacts of herbicides. Here, models have an important role to play. Models can help us to understand mechanisms that are important for the control of weeds. They allow us to test scenarios that are not feasible to test through experiments, and we can use them to determine optimal management strategies. In this Special Issue, we invite submissions on the use of models to address the challenge of improving weed management in agriculture. Topics of interest include, but are not limited to:

  • weed management in the developing world;
  • integrated approaches to weed management;
  • managing herbicide resistance;
  • managing weeds to support ecosystem improvement; and
  • optimized weed management strategies.

Dr. Helen Metcalfe
Dr. Jon Storkey
Dr. Alice E. Milne
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  •  Weed Ecology 
  • Agricultural systems 
  • Weed Management 
  • Resistance 
  • Modelling

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle
The R Package PROSPER: An Environment for Modeling Weed Population Dynamics and the Evolution of Herbicide Resistance
Agronomy 2020, 10(7), 958; https://doi.org/10.3390/agronomy10070958 - 03 Jul 2020
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue The Application of Models for Weed Management in Cropping Systems)
Show Figures

Graphical abstract

Open AccessArticle
DK-RIM: Assisting Integrated Management of Lolium multiflorum, Italian Ryegrass
Agronomy 2020, 10(6), 856; https://doi.org/10.3390/agronomy10060856 - 16 Jun 2020
Abstract
Lolium multiflorum (annual Italian ryegrass) and other grass weeds are an increasing problem in cereal cropping systems in Denmark. Grass weeds are highly competitive and an increasing number of species develop resistance against the most commonly used herbicide modes of action. A diverse [...] Read more.
Lolium multiflorum (annual Italian ryegrass) and other grass weeds are an increasing problem in cereal cropping systems in Denmark. Grass weeds are highly competitive and an increasing number of species develop resistance against the most commonly used herbicide modes of action. A diverse management strategy provides a better overall control of grass weeds and decreases the reliance on herbicides. The bio-economic decision support system, DK-RIM (Denmark-Ryegrass Integrated Management), was developed to assist integrated management of L. multiflorum in Danish cropping systems, based on the Australian RIM model. DK-RIM provides long-term estimations (10-year period) and visual outputs of L. multiflorum population development, depending on management strategies. The dynamics of L. multiflorum plants within the season and of the soil seed bank across seasons are simulated. The user can combine cultural weed control practices with chemical control options. Cultural practices include crop rotation changes, seeding density, sowing time, soil tillage system, and cover crops. Scenarios with increasing crop rotation diversity or different tillage strategies were evaluated. DK-RIM aims at being an actual support system, aiding the farmer’s decisions and encouraging discussions among stakeholders on alternative management strategies. Full article
(This article belongs to the Special Issue The Application of Models for Weed Management in Cropping Systems)
Show Figures

Figure 1

Open AccessArticle
Spatial and Temporal Stability of Weed Patches in Cereal Fields under Direct Drilling and Harrow Tillage
Agronomy 2020, 10(4), 452; https://doi.org/10.3390/agronomy10040452 - 25 Mar 2020
Cited by 1
Abstract
The adoption of conservation agriculture (CA) techniques by farmers is changing the dynamics of weed communities in cereal fields and so potentially their spatial distribution. These changes can challenge the use of site-specific weed control, which is based on the accurate location of [...] Read more.
The adoption of conservation agriculture (CA) techniques by farmers is changing the dynamics of weed communities in cereal fields and so potentially their spatial distribution. These changes can challenge the use of site-specific weed control, which is based on the accurate location of weed patches for spraying. We studied the effect of two types of CA (direct drilling and harrow-tilled to 20 cm) on weed patches in a three-year survey in four direct-drilled and three harrow-tilled commercial fields in Catalonia (North-eastern Spain). The area of the ground covered by weeds (hereafter called “weed cover”) was estimated at 96 to 122 points measured in each year in each field, in 50 cm × 50 cm quadrats placed in a 10 m × 10 m grid in spring. Bromus diandrus, Lolium rigidum, and Papaver rhoeas were the main weed species. The weed cover and degree of aggregation for all species varied both between and within fields, regardless of the kind of tillage. Under both forms of soil management all three were aggregated in elongated patterns in the direction of traffic. Bromus was generally more aggregated than Lolium, and both were more aggregated than Papaver. Patches were stable over time for only two harrow-tilled fields with Lolium and one direct-drilled field with Bromus, but not in the other fields. Spatial stability of the weeds was more pronounced in the direction of traffic. Herbicide applications, crop rotation, and traffic seem to affect weed populations strongly within fields, regardless of the soil management. We conclude that site-specific herbicides can be applied to control these species because they are aggregated, although the patches would have to be identified afresh in each season. Full article
(This article belongs to the Special Issue The Application of Models for Weed Management in Cropping Systems)
Show Figures

Figure 1

Open AccessArticle
Cropping System Redesign for Improved Weed Management: A Modeling Approach Illustrated with Giant Ragweed (Ambrosia trifida)
Agronomy 2020, 10(2), 262; https://doi.org/10.3390/agronomy10020262 - 12 Feb 2020
Cited by 1
Abstract
Weeds present important challenges to both conventional farmers who rely on herbicides and organic farmers who rely on cultivation. Data from field experiments indicate that diversifying crop sequences with additional species can improve weed suppression when either herbicides or cultivation serve as primary [...] Read more.
Weeds present important challenges to both conventional farmers who rely on herbicides and organic farmers who rely on cultivation. Data from field experiments indicate that diversifying crop sequences with additional species can improve weed suppression when either herbicides or cultivation serve as primary control tactics. Here, we report the results of modeling analyses that investigated how cropping system diversification would affect the population dynamics of giant ragweed (Ambrosia trifida L.), an annual dicotyledonous species that is problematic in the central U.S. for both conventional and organic farmers. We found that to prevent an increase in giant ragweed density, the minimum control efficacy needed from herbicides or cultivation used in corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) would be 99.0% in a 2-year corn–soybean system, but 91.4% in a 5-year corn–soybean–rye (Secale cereale L.)–alfalfa (Medicago sativa L.) system. Thus, the diversified rotation would be better buffered against less-than-perfect weed control during corn and soybean phases. Further modeling analyses indicated that the weed suppression effect associated with greater rotation length was attributable not only to increased crop species richness but also to greater temporal variation in planting dates. A planting interval variation index (PIVI), calculated as the coefficient of variation in months between planting activities, was strongly associated with the weed suppressive ability of the rotations we modeled and may be a useful metric for designing other cropping systems. Overall, our results indicate that diversified rotation systems that include both annual and perennial crops are likely to be valuable for managing problematic weed species. Full article
(This article belongs to the Special Issue The Application of Models for Weed Management in Cropping Systems)
Show Figures

Figure 1

Review

Jump to: Research

Open AccessReview
Spatial Modelling of Within-Field Weed Populations; a Review
Agronomy 2020, 10(7), 1044; https://doi.org/10.3390/agronomy10071044 - 20 Jul 2020
Abstract
Concerns around herbicide resistance, human risk, and the environmental impacts of current weed control strategies have led to an increasing demand for alternative weed management methods. Many new weed management strategies are under development; however, the poor availability of accurate weed maps, and [...] Read more.
Concerns around herbicide resistance, human risk, and the environmental impacts of current weed control strategies have led to an increasing demand for alternative weed management methods. Many new weed management strategies are under development; however, the poor availability of accurate weed maps, and a lack of confidence in the outcomes of alternative weed management strategies, has hindered their adoption. Developments in field sampling and processing, combined with spatial modelling, can support the implementation and assessment of new and more integrated weed management strategies. Our review focuses on the biological and mathematical aspects of assembling within-field weed models. We describe both static and spatio-temporal models of within-field weed distributions (including both cellular automata (CA) and non-CA models), discussing issues surrounding the spatial processes of weed dispersal and competition and the environmental and anthropogenic processes that affect weed spatial and spatio-temporal distributions. We also examine issues surrounding model uncertainty. By reviewing the current state-of-the-art in both static and temporally dynamic weed spatial modelling we highlight some of the strengths and weaknesses of current techniques, together with current and emerging areas of interest for the application of spatial models, including targeted weed treatments, economic analysis, herbicide resistance and integrated weed management, the dispersal of biocontrol agents, and invasive weed species. Full article
(This article belongs to the Special Issue The Application of Models for Weed Management in Cropping Systems)
Show Figures

Figure 1

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

(1) PROSPER: a R-package, which simulates weed population dynamics and the evolution of herbicide resistance

Christoph von Redwitz 1,2 *, Friederike de Mol 1

1   University of Rostock, Faculty of Agricultural and Environmental Sciences, Crop Health, Satower Strasse 48, D-18051 Rostock, Germany; [email protected]

2   Julius Kühn-Institut (JKI), Federal Research Centre for Cultivated Plants, Institute for Plant Protection in Field Crops and Grassland, Messeweg 11-12, D-38104, Germany; [email protected]

*   Correspondence: [email protected]

Abstract: Worldwide, weed management is a problem and a challenge for farmers, a problem that is exacerbated by the spread of weed herbicide resistance. Simulation models, combining population dynamics and genetics,are valuable tools for predicting the impact of competing management options on weed density, allele frequency and phenotypic resistance levels. Furthermore, simulation models can be used to derive hypotheses about the underlying genetic effects and interaction of genes. Agricultural scientists often have little programming skills, but are experienced in the analysis of data in R. The new R-package PROSPER provides functions for forward simulation of weed population dynamics on a field scale, selection of individuals according to their sensitivity to herbicides, and recombination of alleles during reproduction. Density dependence, an important characteristic of weed populations, is easily handled by a special function. Objects are provided for entering and saving model parameters in a clear structure, and for saving output data for further processing in R. The basic functions are extensible with R code. PROSPER is based on a model  developed by Renton et al. [1] with stage structured populations. It combines individual-based population dynamics with monogenic or polygenic diploid inheritance and a flexible selection pressure. Stochasticity can be included at all model steps. As a proof-of-package concept, model examples of population dynamics of two annual weed species with herbicide resistance, Echinochloa crus-galli in maize and Galium aparine in winter wheat, are presented. All parameters and the models are available in PROSPER. Apart from simulation, PROSPER is intended for sharing and publishing population dynamic parameters and models. The value of the package will increase when it is extended with models from other scientists. Researchers can adapt contained models for their own research questions and use them for teaching.

Keywords: individual based; metabolic; polygenic; simulation model; weed management

 

(2) Spatial modelling of within-field weed populations; a review

Gayle SOMERVILLE; (main contact) [email protected], (not authorship affiliation)

Mette SONDERSKOV; [email protected]

Niels HOLST; [email protected]

Solvejg Kopp MATHIASEN; [email protected]

Helen METCALFE; [email protected]

Dionissios KALIVAS; [email protected]

Abstract: Spatial simulations using weed models are becoming both much easier to implement, and easier to verify, using comparisons against increasingly reliable observational data. Spatial models require a larger amount of input data, when compared to non-spatial weed models. Firstly, the biological characteristics and specific (or typical) locations of both the weeds and the crop are required. Secondly, if the field is non-uniform in its abiotic characteristics, a spatial map of the relevant characteristics is required, along with information on how those abiotic factors interact with the weed/crop dichotomy. Temporal models also need reproductive and biological spread analysis. Next, the model itself needs to be chosen or designed, parameterised, and implemented. Finally, the model must be verified by comparison with observational data. Field survey observational data gives robust support to spatial model’s construction and parameterisation, thereby helping to verify more long-term model predictions. Additionally, temporal observational data can be compared with alternative model predictions, providing information on alternative causes of weed population changes. This may help identify whether weed population changes are due to external influences such as management or climatic change, or more internal evolutionary changes, including (but not limited to) herbicide resistance evolution.

Back to TopTop