Graham Begg

Coexistence and ecological biosafety of two GM crops in Europe

The three-year, EU-funded project SIGMEA combined skills from many disciplines to examine the biological, environmental, agronomic, economic and legal issues that determine whether GM and non-GM crops can feasibly be grown in the same agricultural landscape. Its conclusions differed for the two crops that have been most widely studied.

Map of field patterning in a study of cross pollination - provided by Enric Mele, SpainSIGMEA reported mainly on coexistence, but also on ecological biosafety. Coexistence refers to the need to separate, in the food production chain, different types of crop, such as those that have been developed with or without genetic modification. While zero impurity of one type of crop in another is impossible to guarantee, the EU had set a threshold of 0.9% GM content for produce that can be labelled as GM-free.

Maths, modelling and quantitative biology

The diverse group of modellers and mathematical biologists in EPI has now reached critical mass with some 15 in-house researchers and students. They direct a range of concepts and tools to questions in systems biology, at scales of organ, individual and community, and in applications as diverse as plant-plant sensing, multi-trophic interactions, ‘industrial’ genotypes and GM coexistence policy.

Modelling and various mathematical approaches now permeate much of the science and some of the applications in EPI. A common and defining feature of the work is the exploration of 'the individual' in 'the system', in which the interactions among individual organisms, organs or cells give rise to emergent properties not predictable from the characteristics of the individuals themselves. Biologists, modellers and software developers combine their skills to address central and essential challenges in modern biology. The examples below are of current work (main funders in parenthesis).

EPI modelling

A lot is known about how plants, animals and human decisions interact within the arable environment. There is increasing evidence however that what happens globally results from the interplay between these processes. Through modelling we aim to combine existing knowledge to make predictions of the system as a whole. The knowledge, methods and tools that we develop provide fundamental support to sustainable agricultural production systems.

Modelling of processes in arable systems

Image showing a model of the root meristematic waveWe use mathematical and statistical modelling to understand the functioning of arable vegetation and organisms, its responses to agricultural innovations and global change, and its role in the sustainability of the arable system as a whole. Key areas of this research include the following.

New directions in the Living Field project

SCRI’s widely respected educational project on the public understanding of science, established with charitable grants of £100k, now provides a range of IT aids, a demonstration garden, all-weather facilities and a study centre. It plans expansion to reach a wider public, while keeping its roots in the excitement of discovery.

Photograph of artist in residence Ronnie Forbes and helper - from the Living Field collectionThe idea of the Living Field project arose in 2001 out of a series of SCRI roadshows, in which scientists met the public in hands-on demonstration and discussion of biodiversity, gene flow, new crops and biological aliens. We learnt there were many people who wanted to know about the fields, food, crops, soil and ecosystems of the arable lowlands, but that roadshows alone would not reach enough people. The Living Field therefore looked to reach a wider audience. A small grant in 2002 allowed us to appoint a first Living Field officer working one day a week, and then to host the secondment of a teacher to SCRI to plan and develop materials. From then it grew.

Clone of Sustainability Research Platform at Balruddery Farm

A new experimental research platform is being established at Balruddery Farm for long-term studies on arable sustainability.Photograph of a poppy field

The overall goal is to test whether or not potential solutions for sustainable agriculture arising from the current RERAD workpackages, actually result in improved arable biodiversity, resilience, crop productivity and yield stability at a commercial, field-scale over at least four rotation cycles (>20 years).

To do this, we will design a sustainable cropping system based on existing research at SCRI that optimises inputs, yield, biodiversity and ecosystem processes. The effect of this ‘sustainable’ system on long-term trends in yield and system health will be tested by comparison with current commercial practice.

Data mining techniques for analysing complex simulation models

Individual-based models of plant populations and communities can be highly complex, reflecting the underlying dynamics of the natural systems under study. Analysing and understanding model behaviour can be extremely challenging. To address this we are collaborating with researchers from the Department of Knowledge Technologies of the Jožef Stefan Institute, Slovenia on the application of data mining and machine learning techniques to the analysis of the IBMs we have developed.

Model analysis

Machine learning methods are being used to analyse the relationship between simulation outputs with IBM inputs (model parameters) in order to gain insight into the behaviour of the model system. Here we rely on a Monte Carlo approach in which simulations are based on IBM parameter values sampled at random from across a predefined parameter space. By applying machine learning methods, we can generalise over the specific simulations made and derive more general rules concerning the behaviour of the system.

Feral and volunteer crop populations in the arable environment

Not all the seed from crop plants is harvested; many seeds are lost, either falling to the ground within the field or dispersed by machinery, birds, etc. to end up beyond the field margins. In some cases the seeds survive in the seedbank giving rise to volunteer populations within subsequent crops or feral populations outside of the cropped area.  The persistence and spread of volunteer and feral populations can lead to significant weed problems while providing a bridge for the dispersal and escape of traits present in cultivated populations.

Volunteer oilseed rape

In the cultivation of oilseed rape (Brassica napus L.), large numbers of seed are shed and fall to the ground before and during harvest. Given the right environmental conditions a proportion of these seeds will become dormant and enter the seedbank, emerging later if subjected to appropriate germination triggers. This has led to the presence of volunteer weed populations within arable fields and to persistent seed bank populations.

Wild arable plants - diversity and function

The ecology and biology of wild arable plants are poorly understood. Of the more than 250 plant species to be found on arable farmland, typically five to 10, among which are wild oat, blackgrass, barren brome and cleavers, constitute the main weed burden of arable cropping. Many of the rest, particularly the broadleaf (dicotyledonous) species, support an arable food web that includes insect groups, mammals and birds. Despite the economic, ecological and aesthetic importance of wild arable plants, embarrassingly little is known about their ecology and genetic diversity.

Our first research paper in this new topic demonstrated the lack of basic information for even the common species (Hawes et al., 2005). If arable cropping systems are to be sustainable, then co-existence between crops and weeds must be managed with minimum or no herbicide application. To achieve this, fundamental and strategic research is necessary into the way wild arable plants respond to crops, weather and field management.

The persistence and spread of novel genes

A transgenic, that is genetically modified, oilseed rape (Brassica napus) provides the model system with which we have investigated the dynamics of novel genes in local populations. A stochastic, spatially explicit individual based model (IBM) was developed to simulate the dynamics of the transgene introduced into a non-transgenic population. The model combines life-history and management processes with environmental drivers to examine the effect of these on the spread and persistence of the transgene and the conferred trait.

The model has been used to explore a number of features of this system:

Gene flow patch diagramSpread and persistence in patchy populations - Plant populations, including arable weeds, typically exhibit spatial heterogeneity, that is patchiness, in their distribution. Together with the localised nature of plant to plant interactions this has the potential to affect the dynamics of a population and the spread and persistence of the genes they posses. Model simulations have shown that small scale spatial heterogeneity in the distribution of transgenic plants combined with localised pollen dispersal reduces mixing between populations and acts to limit the spread of the gene through the population.

The seedbank

Seeds from the arable seedbank - photograph by Gladys Wright/Stewart MaleckiBuried living seed - the seedbank - is central to the composition and succession of disturbed vegetation, allowing regeneration after agricultural or natural clearing of the existing plant cover. In arable-grass systems, the seedbank is the source of both the weed burden and the vegetation that supports the arable food web. Of around 250 species typically found in arable regions, only five to 10 are economically important as competitors to crops. Few of the poisonous species that were once a concern now remain in fields. Most of the other seedbank species have been reduced, in particular the broadleaf or dicotyledonous species that provide food and habitat for detritus feeders, herbivores, parasitoids, predators and pollinators. Knowing the seedbank is therefore essential for balancing the weed burden and biodiversity.

Syndicate content