GM crops have become an option in modern agriculture but they also raise concerns about their ecological and economic impacts. Decisions about GM crops are complex and call for decision support. SIGMEA has been examining decision tools which would help stakeholders and decision-makers to better understand the implications of growing GM crops.

A first model, the so-called “Grignon” model, is a qualitative multi-attribute model for the assessment of ecological and economic impacts at a farm level of GM and non-GM maize crops which was developed together with the EU ECOGEN research project. The model is applied for one agricultural season. This is an ex-ante model developed according to multi-attribute decision tree methodology. In this model, cropping systems are defined by four groups of features: (1) crop sub-type, (2) regional and farm-level context, (3) crop protection and crop management strategies, and (4) expected characteristics of the harvest. The impact assessment of cropping systems is based on four groups of ecological and two groups of economic indicators: biodiversity, soil biodiversity, water quality, greenhouse gasses, variable costs and production value. The evaluation of cropping systems is governed by expert-defined rules.

The “Grignon” model has been used to assess hypothetical and real maize-based cropping systems. For each system, we are able to obtain a qualitative overall assessment together with its ‘profile’, i.e., its performances for the main economic and ecological attributes. Moreover, one can ‘drill-down’ into lower levels of the model to identify the most sensitive components.

 It represents a practical means encapsulating a complex system as it integrates findings of different specific disciplines, such as agronomy, biology, ecology and economics (although it cannot capture specific details of any of these disciplines), and provides a general overview to the assessment of cropping systems which can then easily support discussion among experts and stakeholders.

 The issue of coexistence was also considered: is it possible, under which conditions and to which extent, to grow both GM and non-GM (conventional) crops simultaneously or in close proximity and ensure that non-GM crops would meet a targeted threshold of adventitious presence? As stated above, the answer can be extremely complex as coexistence involves many variable factors, which are difficult to assess, predict and control such as pollen flow, volunteers, feral plants, mixing during harvesting, transport, storage and processing, human error, and accidents. The LandFlow-Gene platform has been designed to assess gene flow at the agricultural landscape level. At present LandFlow-Gene cannot be used on a real-time basis by end-users as quite a lot of data describing landscapes, climate and practices are required. To allow farmers to carry out a preliminary in-field diagnosis, SIGMEA developed a decision-support tool called SMAC Advisor, which is aimed at providing advice to farmers and other decision-makers (advisors, administrative workers, policy makers) who want to assess the achievable level of maize coexistence on a given field and in a given agricultural environment. The assessment is based on a qualitative multi-attribute decision-support model, which was constructed from two sources: (1) MAPOD® gene-flow simulations under constrated situations and (2) expert-provided rules.

SMAC Advisor formulates the decision problem as follows:

Suppose a farmer wants to start growing GM maize on field F. In the neighbourhood, there are some other fields, E1, E2, ¼, En, on which this or other farmers grow (or want to grow) non-GM maize. Then, the question is: to what extent will the plants grown on F genetically interfere with the plants on E’s? Will this interference be small enough to allow coexistence?

The “interference” between plants is expressed and measured in terms of adventitious presence (AP). AP refers to the unintentional and incidental commingling of trace amounts of one type of seed, grain or food product with another. EU regulations have introduced a 0.9 % labelling threshold for the AP of GM material in non-GM products (Regulation 2003/1830/EC). Thus, in order to approve the coexistence between GM and non-GM crops, we usually require that the achieved AP is 0.9 % or less. Now, some supply chains may require lower levels of AP (e.g., organic farming). In SMAC Advisor, the target threshold is a user-defined parameter.

SMAC Advisor requires basic information from the user about the: (1) emitting field F, (2) neighbouring fields E1, E2, ¼, En, (3) relation between F and each Ei in terms of distance, relative size, prevalent wind direction, etc., (4) type and characteristics of used seeds, (5) environmental characteristics (e.g., background GM pollen pressure), and (6) use of machinery (e.g., sharing with other farmers). All these elements can easily be provided by the end-user (e.g., farmers) through a user-friendly interface (figure 4).

On this basis and through a multi-attribute decision tree (figure 5), SMAC Advisor determines the achievable AP, that is, the expected level of GM impurities in harvests of the neighbouring fields, and compares it with the required target AP, which is provided by the user. SMAC Advisor completes the analysis giving one of the following “colour-coded” recommendations: (1) “Green”: GM farming allowed or possible, (2) “Red”: GM farming disallowed, (3) “Yellow”: coexistence is possibly achievable but further risk assessment is needed, and (4) “Orange”: the target AP is currently not achievable, continue assessing additional coexistence measures.

Figure 4. Description of SMAC Advisor user interface




Figure 5. Description of SMAC Advisor hirarchical attribute structure