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Modelling weed crop dynamics and competition to improve long-term weed management - AR0407

Description
Modelling weed:crop dynamics and competition
Currently many farmers tend to use herbicides over-cautiously (because of concerns about long-term consequences and of the reliability of threshold values), resulting in a supra-optimal level of weed control, which in the long-term is unsustainable (economically and environmentally). In order to promote more optimal (reduced) herbicide usage, which is the primary MAFF policy objective, there is a need to provide a better understanding of weed-crop competition and weed population dynamics in order to predict the short and long-term repercussions of changing weed management practice. To this end the project will extend simulation models to include environmental stochasticity and to bring together weed:crop competition with weed:crop dynamics under a single model framework. The project will work a range of winter and spring sown crops, and will target weed species that have been identified as valuable for conservation by a recent MAFF funded project ‘The impact of herbicides on weed abundance and biodiversity’ (PN0940).
Developing predictive and stochastic models of weed:crop dynamics and competition;
The priority will be the development of competition and stochastic population-dynamics models, based on the knowledge that exists about environmental impacts on weed and crop growth and on weed recruitment and seed fecundity. This model will provide the tools for integrating increasing understanding of detailed processes into longer-term prediction. This area of modelling will integrate with other parts of this project and will also be a key link between other projects in the weed biology programme. This area will include some element of study on the role of changing crop canopy characteristics on the weed:crop competitive balance.
1) Establishing the ecophysiological basis of functional groups
This section of work within the modelling weed:crop dynamics and competition area will play two roles; Firstly it is a great opportunity to get to grips with the concept of functional groups and to approach the topic in a truly quantitative way. The project will measure specific physiological, phenological and partitioning parameters that have been demonstrated to be significant in growth and fecundity of arable weeds and will also collate broader ecological traits. The analysis will look at separation of weed species on the basis of these parameters/traits into function groups, and will also focus on trade-offs between different specific physiological parameters.
Secondly, it is also a real chance to undertake an intensive project of parameterisation for weed species which have been largely neglected in the past, and a very important part of the work will be gathering key parameters for a wide range of UK weed and crop species, which can be used in modelling studies.
2) Validation of predictions (ADAS sub-contract)
There is a continuing need to validate the predictions of models, and the results of decision making based on them. Validation field trials will be established by ADAS (as a sub-contractor) which will build on previous work and extend the evaluation to include multiple crop species and an assessment of the effect of decisions in following crops – through weed seed production.
Objective
To develop, and test, models of weed:crop competition and dynamics, and of weed ecological groupings, to improve decision making in long-term weed management.
1a) Development of models;
Extend existing mechanistic process modelling (INTERCOM) approach to a wider range of UK weed and crop species. Develop a generic model framework for combining detail about crop:weed competition and other specific environmentally influenced processes, with longer-term descriptions of weed population dynamics. Modelling approaches must be capable of including existing and increasing information about the (environmentally) stochastic nature of the system.

1b) Changes in crop canopy characteristics and competitiveness;
Assess the relative competitiveness of crop species and identify the crop canopy characteristics that are associated with increased weed supressiveness. Establish and document the changes that have taken place over time in the canopy architecture and other canopy characteristics of winter wheat cultivars. Re-analyse an existing data-set (from the ‘Broadbalk’ winter wheat) to quantify the effect of changing crop cultivar competitiveness on patterns of crop yield loss due to weed competition. Compare the degree of variability in competitive ability between cultivars of winter wheat with the variation that is associated with contrasting crop species.

2) The ecophysiological basis of functional groups;
Measure key physiological parameters (including phenology, gas exchange and biomass partitioning) for a wide range of UK weed species. Collate a database of wider ecological traits from the literature and using experimentation where necessary. Undertake multivariate analysis of the entire parameter/trait dataset to identify groupings of species (‘functional groups’) and in parallel use regression techniques to study trade-offs in the values of specific physiological parameters.

3) Validation of predictions (ADAS sub-contract);
Undertake field experiments in key UK crop species to evaluate predictions of crop yield loss from weed populations, use the data generated by field experiments to make more precise model parameter estimates and refine future predictions. Follow the fate of field plots to study the longer-term implications of different weed management practice. Contrast the fate of the same weed populations in spring and winter sown crops.
Project Documents
• Final Report : Modelling weed crop dynamics and competition to improve long-term weed management   (334k)
Time-Scale and Cost
From: 2001

To: 2005

Cost: £968,333
Contractor / Funded Organisations
Rothamsted Research (BBSRC)
Keywords
Arable Farming              
Crops              
Farming              
Natural Resource Use              
Peer Review              
Sustainable Farming and Food              
Sustainable Production              
Weed Control              
Fields of Study
Arable Crops