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management of critical points by stakeholders
Baptiste Lecroart, Marianne Le Bail
To cite this version:
Baptiste Lecroart, Marianne Le Bail. Independant management measures : Identification and man- agement of critical points by stakeholders. 2007. �hal-02818017�
Project no. 501986 Project acronym SIGMEA
SIGMEA Sustainable Introduction of GM Crops into European Agriculture
Instrument: STREP
Thematic Priority: FP6-2002-SSP1 (Policy Oriented Research)
D7.3 Independent management measures
- Identification and management of critical points by stakeholders -
Due date of deliverable: 3rd july 2006
Actual submission date: 14th december 2007
Start date of project: 3 May 2004 Duration: 42 months Organisation name of lead contractor for this deliverable: CETIOM / INRA
Revision [1]
Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006) Dissemination Level
PU Public
PP Restricted to other programme participants (including the Commission Services) X RE Restricted to a group specified by the consortium (including the Commission Services)
CO Confidential, only for members of the consortium (including the Commission Services)
EXECUTIVE SUMMARY - SIGMEA Deliverable 7.3
Whereas simulations only focused on the main characteristics of landscapes and cropping systems affecting gene flow, this deliverable introduces information on the management of coexistence, and the professional actors' point of view (particularly farmers and collecting firms): what do they consider as "critical point"? What are the measures capable to comply with grain quality standards, and what are their efficiencies? What are the main constraints and rooms for manoeuvre when applying these measures? Information came from bibliography but also from investigation made in Beauce and Alsace: surveys of farmers, surveys of collecting firms on their segregation practices, and working group gathering different stakeholders (farmers, agricultural work contractors and collecting firms and agricultural adviser). The working groups were based on METAPLAN methodology:
Stakeholders were asked to write their ideas on pieces of paper and these papers were then discussed together and classified into groups. Two questions were debated: (i) What problems do you think you may be faced with if GM varieties are introduced in your region? and (ii)
“What strategy could be set up to manage coexistence?”. Results of simulations were used to stimulate discussion.
Critical points identified by stakeholders go beyond the fields and cropping systems: they take into account social acceptance and the whole food chain, particularly the interface between farms and grain collectors. Grain quality is already managed (seed production, waxy maize, erucic rape, mycotoxin...), and this ability could be used for the purpose of GM / non GM coexistence. Nevertheless, this case is different because the farmer is not always liable for the quality of his own production (spatial aspects), though he could suffer economic consequences.
The measures could be preventive (acting before sowing in order to prevent gene flow) or curative (acting when admixture on field has already occurred). The efficiency of these measures depends on the context, as it has been shown by simulation results.
Concerning feasibility, three levels of management were investigated: technical choices at the field level (e.g.: changing sowing dates); crop allocation in space and time (e.g.:
implementation of separation distances); strategic choices at the farm level (e.g.: new equipment for harvesting). Rooms for manoeuvre not only depend on these levels, but should also be assessed on a case by case basis, measure by measure. Some of them depend on the farm, particularly for those involving changes in crop allocation, and thus could result in different costs. Measures concerning crop allocation would require a degree of inter farm coordination depending on farm spatial patterns. Information collection and dissemination within the farming region are very important.
Working groups showed that traditional rules of good neighbourhood allow a flexible interpretation of the liability regime and thus make the implementation of bilateral measures easier, even if the non GM farmer has to act or could be encroached (e.g. compensation for the implementation of buffer strips or for harvesting discard zones). On the contrary, the working groups acknowledged the unanimity is very difficult to achieve in case all farmers have to act together (e.g. cropping block strategy).
Some specific points came out from investigations carried out in Beauce (OSR) and Alsace (maize).
Alsace
• High purity rates are required by the food industry (starch and semolina).
• Strategies involving the establishment of separation distances between GM and non- GM maize in Alsace are quite efficient but would be difficult to implement, as the average field size is small and the density of maize is high.
• Farmers are not willing to accept the imposition of mandatory rules concerning the timing of operations (sowing, harvesting). Depending on the farms, it would be possible to delay sowing by two to four days.
• Stakeholders involved in the maize supply chain may have opposite interests, particularly concerning timing of management practices for coexistence. At harvest, farmers would prefer silos to be allocated to particular products on a spatial, rather than temporal, basis. This strategy would require co-ordination between collecting firms, but there is a high level of competition between collecting firms.
• It would be possible to implement a non-GM buffer zone (in the GM field, at the sowing step) and/or a discard zone (in the non-GM field, at the harvesting step) for the first few rows, which are currently sown in strips at the edge of the field.
Beauce
• Coexistence between GM and non GM OSR is not a real issue at the present time, but the management of grain quality is already an issue for erucic OSR.
• Stakeholders carefully consider volunteer management, including management throughout crop rotations
• The number of unknown variables make it difficult to predict the design of coexistence in the Beauce context (relative prices, cost and technical feasibility of analyses, thresholds required by food industry)
• The question of whether testing grain for % GM should occur at the entrance or the exit from the silo was strongly discussed in the general debate in order to determine whether if it is preferable to manage at the field or the silo level.
• The implementation of non GM buffer zones would be possible even for HT GM OSR, but it would be more efficient if sprayed by non GM farmer.
According to simulation results and information mentioned above, four pre-scenarios have been ranked, depending on: (i) the threshold for the maximum GM adventitious presence expected in non GM harvest (maize only) (ii) the ratio GM / non GM crop. The pre-scenarios were then described in terms of (i) adventitious presence risk for non GM crops (assessed from model out puts and stakeholders expertise) and (ii) management rules to be implemented on individual farm and/or collective levels.
In pre-scenario 1 there are low proportions of GM crop in the landscape and a high threshold.
The risk of exceeding the threshold in the non GM silo is low and the level of GM crop in the sub-region is the only information needed by collecting firms to manage coexistence. For the pre-scenario 2 the risk of exceeding the threshold is low with only a few non GM fields above the threshold and responsible for increasing %GM content in the non GM silo. To manage coexistence, post-sowing measures may be sufficient (decreasing admixture due to machinery, selecting fields of interest and fields at risk and allocate them to the right outlets).
For pre-scenario 3 the risk of exceeding the threshold is high. Specific measures must be set up before sowing to decrease GM pollen dispersal (buffer zones/separation at the field level, and GM and non GM field allocation (separation distance, gathering crops)). In the pre- scenario 4 the level of GM crop is so high and the thresholds so stringent that the risk of
whole silos being over thresholds is high and the management of coexistence not sustainable for stakeholders. The limits between pre-scenarios depend on the characteristics of sub- regions. The following table gives the example of the Alsatian case studies.
Table 1 : Pre-scenarios for coexistence for Maize in Alsace
Thresholds* required by non GM stakeholders
Heiwiller Ensisheim
% GM / total
maize 0.9 0.4 0.1 0.01 0.9 0.4 0.1 0.01
10% 1 1 3 4 1 1 3 4
25% 1 3 3 4 1 2 3 4
50% 2 3 4 4 2 3 3 4
75% 3 3 4 4 2 3 4 4
*Maximum GM adventitious presence in non GM
The case of OSR is different. According to the model, where GM and non-GM varieties are not grown in the same field over years, pre-scenario 1 could be achieved in Beauce and Fife at the 0.9% threshold for a GM introduction rate below 50%. However, the pre-scenarios cannot be seen in a static way, because of volunteers' dynamics in cropping systems. For example, the possibility of returning to non GM OSR after GM in certain fields would justify sharing fields between two groups: those where GM was grown in the past, and the others. The first group would justify special measures for volunteer management, whereas the second could be managed in the above described pre-scenario. Nevertheless, in case of bad volunteer management, limits between pre-scenario may change, due to cumulative effects over time.
In conclusion the characteristics of a territory, including farmers fields and silos, managed by stakeholders (farmers, contractors, crop collectors) implementing different strategies, will be important in determining the success of the coexistence policies. The adjustments necessary for coexistence depend not only on structural constraints (field pattern, cropping systems), but also on organizational ones. These constraints must be taken into account in order to manage coexistence in each region.
D7.3 Independent management measures
- Identification and management of critical points by stakeholders -
Maize case studies
B. Lécroart, M. Le Bail - INRA
SUMMARY
Introduction ... 3
I. Methods ... 3
I.1. Surveys and working group... 3
I.1.a. Farmers survey ... 3
I.1.b. Collecting firms survey ... 4
I.1.c. First Working Group ... 4
I.2. Risk assessments used in the surveys and working group... 5
I.2.a. Simulations of the impact of the characteristics of each non-GM and GM field on cross-pollination rate ... 6
I.2.b. Simulations of the effect of flowering time lag on adventitious GM presence in the non-GM harvest... 6
II. Results ... 10
II.1. Identification and hierarchical organisation of critical points... 10
II.1.a. Public opinion ... 10
II.1.b. Market ... 10
II.1.c. Field and inter-field ... 11
II.1.d. Farmers and agricultural work contractors... 11
II.1.e. Collecting firms... 12
II.1.f. Relationship between collecting firms and farmers ... 12
II.2. Management of critical points... 15
II.2.a. Measures for limiting GM volunteers and seeds... 15
II.2.b. Pre-sowing measures... 15
II.2.c. Measures for selecting the harvest of interest and limiting other risks of admixture... 25
II.2.d. Conclusion concerning the assessment of measures ... 28
II.3. Pre-scenarios ... 31
II.3.a. First pre-scenario... 31
II.3.b. Second pre-scenario ... 31
II.3.c. Third pre-scenario ... 32
II.3.d. Fourth pre-scenario ... 32
Bibliography ... 33
Appendixes ... 34
Appendix 1. Assessment of variation in the rate of cross-pollination due to environment ... 34
Appendix 2. Number of “emitting field x recipient field” pairs in each group, classified according to their main characteristics... 36
Appendix 3. Maximum cross-pollination rates calculated for each group of “emitting field x recipient field” pairs, classified according to their main characteristics... 38
Introduction
We have shown above how models can be used to assess the effect of different structural variables on GM dissemination (landscape shape, wind, frequency of maize in the agricultural used area (AUA)). Nonetheless, such modelling approach involves a simplification of reality.
We focus on a specific step in the maize production chain. MAPOD is a gene flow model, simulating pollen exchange between GM and non-GM maize crops, to assess the risk of admixture due to cross pollination. Previous studies have shown that there are other possible sources of admixture, such as the machinery used at various steps in the maize production chain (sowing, harvesting, transport and adventitious GM presence due to post-harvest operations — Messéan et al., 2006). Furthermore, all the actors involved in the maize supply chain interact at each stage of maize production. The individual and collective choices made by these actors may affect the level of admixture. For this reason, we used a combination of simulations, surveys of farmers and cooperatives and a working group bringing together various actors in the maize chain production, to organise the critical points of coexistence between GM and non-GM maize into a hierarchy and to assess the feasibility of coexistence measures. This study was conducted between 2006 and 2007 in Alsace.
We will first present the methods used and will then discuss the identification of critical points. This will lead on to a discussion of the feasibility of independent measures for ensuring coexistence. We conclude this part of the report with the first pre-scenarios, taking into account assessments of the risk of admixture and some significant risk management tools.
I. Methods
Surveys of collecting firms and farmers and the working group aimed to identify critical points and to organise them into a hierarchy. These methods are presented below (section I.1).
As GM maize had not yet been cultivated in Alsace, the surveys and working groups were based on simulations of the coexistence of GM and non-GM maize in a landscape. These simulations are described in section I.2.
I.1. Surveys and working group I.1.a. Farmers survey
A farm survey was conducted to assess the possibilities for the coexistence between GM and non-GM maize crops on the same farm (Duquesne, 2005). Previous studies have focused on the effect of changes in agricultural practices designed to limit gene flow (Messéan et al., 2006). However, the agronomic and organisational constraints resulting from these changes in practice had not been studied at the level of individual farms. In this study, we aimed to explore the conditions for recommended practices for limiting gene flow and to identify the room for manoeuvre at farm scale.
We interviewed various farmers from Alsace, focusing on two agricultural areas in particular:
the Plaine de l’Ill and the Sundgau (see D7.1 Alsace). These two regions differ considerably in terms of pedoclimatic constraints, organisation and maize cultivation (see D7.1 Alsace).
The sample was based on a database of farmers who routinely deliver their crops to country elevators of the cereal group Cooperative agricole de cereales (CAC). The sample of farms was roughly representative, according to the classification of Haut-Rhin farms (Table 1).
The survey was carried out in two stages. We began by interviewing farmers to obtain a general description of their farms and to identify the rules governing decision-making (Aubry et al., 1998; Capillon et al., 1991). The main descriptors of maize cropping systems were collected in this step, for use as input data for MAPOD. Twenty-four farms were surveyed in this first step, some of the results of which are presented in Alsace D7.1. We then carried out a more detailed survey of the same farms, focusing specifically on the coexistence issue at farm scale. The farmers were asked to consider the risks of admixture between GM and non GM crops from sowing to harvesting, using the results of simulations with MAPOD. Taking into account their own field pattern, the farmers were asked to consider the feasibility of coexistence measures. Twenty-one farmers were interviewed in this second step (Duquesne, 2005).
I.1.b. Collecting firms survey
The maize produced by farmers is mostly collected by country elevators, where crops are dispatched into several batches for different agroindustrial markets with different quality gradings. The issue of coexistence therefore raises potential problems at the level of primary production, and the management of coexistence at this level influences farmers’ practices.
The collection management of an Alsatian collecting firm (Coopérative Alsacienne de Céréales – CAC) was studied (Raveneau, 2005; Coleno et al., 2005). This study focused on the separation of maize batches for starch and meal production. Several managers dealing with different levels of crop collection— crop planning management and relationships with farmers, collection and transport scheduling, marketing and managers of collection silos, storage silos and dryers — were interviewed.
I.1.c. First Working Group
In previous surveys, we studied the issue of coexistence at the farm and collecting firm scales, focusing in particular on the room for manoeuvre of each stakeholder. However, coexistence issues also concern the interactions between stakeholders (interactions between farmers,
Table 1. Representativeness of the sample of farmers
Type
Proportion of the AUA producing
maize grain in Haut-Rhin
Proportion of farms producing maize grain in Haut-Rhin
Proportion of farmers interviewed
in the survey
Grazing livestock 5 % 7 % 4 %
Diversified farms 7 % 5 % 4 %
Dairy 12 % 13 % 29 %
General arable 72 % 62 % 63
Chambre d’agriculture du Haut Rhin classification, based on expert opinion and the results of the 2000 agricultural census.
96% of the grain maize area is grown on four types of farm:
(i) Grazing livestock farms: more than 8 large animal units with no milk quota
(ii) Diversified farms: farms producing maize and specialist crops (grapevine, tobacco, asparagus etc.) and/or animals (sows, hens, horses etc.)
(iii) Dairy farms: milk quota > 20000 litres
(iv) General arable farms no more than 8 units of grazing livestock, no specialist crop or animal production, no milk quota.
between farmers and collecting firms, and between collecting firms). The approach described above cannot take these interactions and their dynamics into account fully. We therefore organised a first working group, bringing together stakeholders from the maize production chain in Alsace. The objective of this working group was to discuss risks and risk management relating to coexistence.
This working group brought together collecting firms (five people from three different collecting firms), an adviser and some farmers (4). All the farmers had already been interviewed in the previous step. The sample of farmers belonging to this working group cannot be considered to be representative of all farmers in this region. They were, nonetheless, highly diverse: a single-crop maize producer, a farmer from a diversified farm (maize, wheat, other crops), a cattle farmer, and an agricultural work contractor. All the main collecting firms of the region attended the first working group meeting. This meeting took place in Colmar (Alsace) in 2005, on a single day divided into two periods. The first period was devoted to the identification of critical points. The participants were asked the following question: “What problems do you think you may be faced with if GM varieties are introduced in Alsace?” The second part of the meeting dealt with the management of coexistence, with participants asked “What strategy could be set up to manage coexistence?”
We used a metaplan method: each period was divided into two stages. In the first stage, each participant was asked to write his own ideas on pieces of paper, with one idea per piece of paper and no limit on the number of pieces of paper per participant. The ideas on the pieces of paper were then discussed together and classified into groups, with the pieces of paper pinned to a board. At the end of the first metaplan, the MAPOD model and some simulation results were presented.
I.2. Risk assessments used in the surveys and working group
The stakeholders have not yet been faced with the introduction of GM maize and may lack knowledge about the level of risk associated with the adventitious presence of GM maize. We therefore presented previous results on coexistence, the MAPOD model and the results of simulations at field and landscape scale both during interview and during the working group meeting, to illustrate and stimulate discussion and debate. Stakeholders were able to assess the results of simulations, by comparisons with their own experience (waxy maize or seed production, for instance).
Various types of risk assessment were used: (i) A previous case study published by the JRC/IPTS-ESTO Consortium (Messéan et al., 2006); (ii) simulations carried out for the
Table 2. Cases considered in the simulation carried out for the survey
Parameter Cases considered
Area of the field 0.5, 1, 5 and 10 ha Flowering time lag 0, 2, 4 and 6 days
Isolation distance between fields 0, 10, 20, 50, 75, 100, 150, 200, 300 and 400 m
Layout of GM field with respect to non-GM field
North, North-East, East, South-East, South, South-West, West and North- West.
Simulations were based on the meteorological conditions prevailing in Alsace (meteorological station at Colmar-Meyenheim – see 7.2). Several cases were simulated, as a function of field area (square shape), flowering time lag, the distance between fields and wind direction with respect to the non-GM and GM field layout.
survey in a simple one-field to one-field design (Table 2); (iii) simulations performed in Alsace (7.2) or promoted by discussions with stakeholders (e.g. differences in flowering time between GM and non-GM crops, or flowering time lag).
I.2.a. Simulations of the impact of the characteristics of each non-GM and GM field on cross-pollination rate
We used simulations already carried out for Alsace to describe situations in which coexistence is risky. We associated the cross-pollination rate in each “GM emitting field/non-GM recipient field” pair with the main characteristics of the pair of fields concerned (Box 1). The composition of the pollen cloud in the air around the non-GM field determines the cross- pollination rate. It depends not only on the receiving and emitting fields, but also on the neighbouring fields. We showed that this environment (neighbouring fields) had little effect on the variability of cross-pollination rate, which ranged from 2% to 15% of the cross- pollination rate (Appendix 1).
I.2.b. Simulations of the effect of flowering time lag on adventitious GM presence in the non-GM harvest
In previous simulations in Alsace (see D7.2 Alsace), we assumed that GM and non-GM varieties flowered at the same time. This hypothesis may be considered the worst-case scenario, as it implies that all the pollen is released while the silks are receptive. We therefore did not take into account differences in flowering time between two fields or between male and female flowers in a given field.
Further simulations were carried out to assess the effect of flowering time lag on the adventitious presence of GM maize in the non-GM harvest:
• We first considered the natural flowering time lag: it corresponds to the differences in flowering time between fields currently observed in Alsace, with no change in farming practices.
• We then simulated different decision-making rules designed to create a difference in the flowering times of GM and non-GM crops.
We assessed the effect of variability in the natural flowering time lag, by selecting three allocations of GM and non-GM maize to fields that had already been simulated (D 7.2 Alsace). Several different flowering dates were assigned to each field (Box 2). We then assessed various rules designed to create a difference in the flowering times of GM and non- GM crops, by selecting one allocation of GM and non-GM maize to the fields. We considered the same allocation of flowering dates. For each allocation, the onset of male and female flowering in the GM fields was modified, being brought forward or delayed by two and four days with respect to the previous simulations. This made it possible to simulate rules concerning a time lag in the flowering of GM and non-GM maize.
Characteristics of the field ip * field iq pair:
- Distance
- Area of donor field (ip) - Area of recipient field (iq)
- Location of donor field with respect to recipient field
n1: number of GM fields n2: number of non-GM fields
Box 1. Data used to determine the effect of the characteristics of each non-GM and GM field on cross-pollination rate Batch of
simulation number
Levels and allocation of maize
1 Random allocation of maize: 25% / 50% / 75% / 100%
2 Random allocation of maize, according to the 2000 French agricultural census: 50% in Heiwiller and 60% in Ensisheim 3 Maize allocated according to the CAP statements of farmers in
2005
GM fields
i1 ip in1
j1
jq
non-GM fields
jn2
For each simulation:
Adventitious GM presence in the non-GM harvest of field jq due to GM pollen from field ip SIG data
Number of repetitions *
Proportion of total maize area under GM
maize Landscape
Proportion of maize
in AUA
10% 50%
Heiwiller 50% 80 80
Ensisheim 60% 80 80
* allocation of GM and non GM maize to the fields
For each GM field,
Onset of female flowering
=
Onset of female flowering in step 1 - 4 days
- 2 days + 2 days + 4 days
Decision-making rules concerning flowering periods
1 allocation of GM and non-GM maize (A):
The same allocations of onset of female flowering are considered
Box 2. Simulations carried out to assess the effect of flowering time lag on cross-pollination rate
Batch of simulation
number
Levels and allocation of maize
1 Random allocation of maize: 25% / 50% / 75% / 100%
2 Random allocation of maize, according to the 2000 French agricultural census: 50% in Heiwiller and 60% in Ensisheim 3 Maize allocated according to the CAP statements of farmers in
2005
3 allocations of GM and non-GM maize are selected:
A, B & C
(Heiwiller with 50% of total maize area under GM maize)
3 allocations of GM and non-GM maize (A, B & C):
Onset of female flowering is randomly allocated, according to a normal distribution with a standard deviation of 3 x
Number of repetitions: A (40), B (10) & C (10)
* The survey showed that most maize fields flowered during the second half of July in both regions studied (see D7.1 Alsace)
For each field:
Flowering dynamics from MAPOD model
One day protandry **
** with a plant density of 8 x 104 and assuming an absence of drought stress (Angevin et al., 2007)
1
2 Natural flowering delay
4 x 40 = 160 simulations
Figure 1. Classification of items during the working group
market Public opinion
Cooperative / elevator
Interaction cooperative /
farmers Farmers and
contractor
Field and inter-field
market Public opinion
Cooperative / elevator
Interaction cooperative /
farmers Farmers and
contractor
Field and inter-field
II. Results
II.1. Identification and hierarchical organisation of critical points
This section deals with the way in which stakeholders discussed critical points. The various ideas about critical points proposed by stakeholders during the first Metaplan were written on pieces of paper and classified into six groups: (i) public opinion, (ii) market, (iii) field and inter-field, (iv) farmers and agricultural work contractors, (v) collecting firms and (vi) interactions between collecting firms and farmers (Figure 1). The participants of the working group belong to the last four of these groups and have no influence on the first two of these groups (public opinion and market).
At the end of the first Metaplan, participants were asked to organise the groups into a hierarchy. Market was deemed the most important and public opinion was considered the least important, whereas in the very first exchanges between participants the negative public opinion concerning GMOs was advanced as a huge problem. The stakeholders generally considered Field and inter-field to be a minor critical point. The other three groups were all considered to be strategic. Nonetheless, this classification should be considered with care, as it differed significantly from one stakeholder to another.
II.1.a. Public opinion
The residents of Alsace are becoming less tolerant of maize farming, particularly as maize accounts for a large proportion of the AUA in Alsace. Thus, participants were afraid that the use of GM varieties would provide the Alsatian people with yet another cause for complaint concerning maize crops. Participants were worried out maize crops being rejected. They raised the issues of consumers not wanting to eat foods containing GMOs and the risks of GM crops being destroyed by demonstrators.
II.1.b. Market
The economic issues associated with the adoption of GM maize were often raised. The
“market” group was considered the most important overall by the stakeholders of the working group. Indeed, increasing profitability would be the main decision-making rule governing the adoption of GM crops by farmers: “Do GM crops make it possible to increase the profitability of maize crops?” The profitability of GM crops depends on market characteristics (volume, demand and price) and the characteristics of GM varieties.
According to country elevators, the costs of coexistence depend heavily on the maximum threshold set for the adventitious presence of GM maize in the non-GM harvest. This issue is particularly important, given that most Alsatian maize is sold for human consumption (semolina and starch industry). These outlets require a high purity rate, with very low limits on the adventitious presence of GM maize: 0.1% for the starch industry and 0.01% for the semolina industry. Stakeholders are more concerned about these thresholds than the 0.9%
legal threshold when dealing with coexistence.
Stakeholders identified three major risks associated with the introduction of GM maize in Alsace:
• The brand image of Alsatian maize could be damaged, harming the more profitable outlets or resulting in a general decrease in the selling price of maize.
• Stakeholders would have to separate non-GM maize requiring a high purity rate (0.1% for the starch industry and 0.01% for the semolina industry) from sowing
(ensuring seed purity) to storage. This would decrease the profitability of the non-GM maize production chain. This argument has been raised in several previous studies (Valceschini and Avelange, 2001; Le Bail and Valceschini, 2004).
• Some of the non-GM production would not comply with the high purity thresholds required. This harvest would be directed to less profitable markets.
II.1.c. Field and inter-field
The risk of cross-pollination between GM and non-GM fields was little considered by stakeholders, possibly due to a lack of knowledge concerning this risk. Stakeholders generally have to deal with much lower requirements in terms of purity rates. For example, purity rates of only 96% are considered acceptable for waxy maize. Thresholds of this type rarely required management at field scale. The small size of fields in Alsace (particularly in the Sundgau region) also complicates the issue of coexistence, as it makes it difficult to implement coexistence measures, such as the maintenance of isolation distances between GM and non- GM maize.
After a short presentation of the results simulation on a 1000 ha landscape, the market manager of a country elevator concluded that the 0.01% threshold could not be achieved, even if the area covered by transgenic varieties was small (10%). This conclusion is probably correct if GM crops are evenly spread over the entire region, but this is not necessarily the case if GM crops are concentrated on one side of the region, with non-GM crops concentrated on the other side of the region.
II.1.d. Farmers and agricultural work contractors
The stakeholders of this group identified two key sources of GM adventitious presence:
• Cross-pollination from neighbouring GM fields and the impact of the distribution of GM and non-GM fields in the landscape,
• The sharing of machinery between GM and non-GM fields.
Spatial allocation of crops in the landscape
We found in previous groups that stakeholders found it difficult to manage coexistence at the level of individual fields. They were more eager to address the coexistence issue at a larger scale, corresponding to individual farms or supply areas, corresponding to several farms. At this scale, they identified two critical points in Alsatian systems: (i) the farms are scattered:
“An individual farm may cultivate fields in various municipalities”, and (ii) there is a high density of maize in the AUA.
Machinery
Planting and harvesting are particularly crucial in terms of the risk of admixture due to machinery.
Agricultural work contractors play a key role in the maize production chain in Alsace, as there are many small farms and part-time farming is common in this region. There is strong competition between agricultural work contractors, resulting in these contractors having little economic room for manoeuvre. Moreover, labour demands are high for these two tasks, particularly as the Alsatian climate strongly constrains the time window in which they can be performed. Thus, agricultural work contractors are faced with strong labour constraints.
At sowing, there is a risk of admixture due to a mix-ups concerning seed bags (GM and non- GM). If a non-GM crop is drilled or harvested after a GM crop, GM adventitious presence
may result from significant numbers of GM seeds being left in the sowing machine or GM grains being left in the combine harvester. In addition, the cleaning of machinery is a time- consuming activity. Stakeholders spoke about the root worm crisis in Alsace, when measures such as the cleaning of combine harvester were implemented to combat this pest. The clear message emanating from this discussion was that it is impossible to clean combine harvesters fully between operations, as this operation requires an entire working day.
Thus, agricultural work contractors face real time constraints and all time-consuming tasks, such as the cleaning of machinery, lead to a decrease in profitability.
II.1.e. Collecting firms
Collecting firm infrastructures are saturated in terms of labour requirements and volumes during the collecting period. They are currently trying to extend the collecting period over a longer time window, by paying more for early and late deliveries. Collecting firms have limited separation capacities, particularly in Alsace, where they have invested in large- capacity silos. This leads to a risk of mixing up GM and non-GM crops, within the same silo, at several steps (collection / drying / storage). The risk is higher if GM and non-GM maize are present at the same site. The storage step is even more critical than the collection step.
There is a high density of collection silos in Alsace, but the number of dryers is limited.
Furthermore, maize must be dried within 24 hours of its arrival at the collection silo. Country elevators aim to concentrate the flow of a particular type of product.
Collecting firms have suggested that segregation could be ensured by grouping together deliveries of similar products on a chronological basis. This would lead to delivery dates for each kind of product being imposed on farmers. Farmers are opposed to this strategy, as it may not be possible to deliver the harvest on time if the weather is bad. Moreover, it would be necessary to clean combine harvesters between the GM maize delivery period and the non- GM maize delivery period. Farmers therefore prefer product delivery to be grouped on a geographical basis, with collection silos allocated to one product throughout the whole collecting period. Collecting firms are reluctant to implement such a strategy as it would require co-ordination between different firms. There is strong competition between collecting firms in Alsace, which might make it difficult to obtain agreements between collecting firms on the sharing of infrastructures.
II.1.f. Relationship between collecting firms and farmers
The interests of collecting firms conflict with those of farmers and agricultural work contractors. Collecting firms would like to spread out maize deliveries, whereas agricultural work contractors are eager to harvest as quickly as possible, to maximise profitability from their machines.
The issue of seed purity was debated. Collecting firms have been responsible for checking the purity of non-GM seeds and bear the cost of these analyses.
Stakeholders identified the level of information of the various actors in the maize production chain as a key issue:
• Information about the distribution of GM crops at the landscape scale (“Where is GM maize drilled?”). Farmers will have a legal obligation to state whether they grow GM crops. The stakeholders asked: (i) Who has access to this information and will it be available to other farmers and/or collecting firms? (ii) When is this information available (before or after sowing)?
• Information about the type of maize delivered: The person delivering the maize to the farm (unless it is the farmer himself) may not necessarily know what type of maize is
being delivered. Other members of the household, such as the farmer's mother or father, may pick up the maize and deliver it to the farm.
• Information about the level of GM adventitious presence in the non-GM harvest:
collecting firms currently have no rapid and cheap method for assessing adventitious GM levels in a trailer of non-GM grain. Some stakeholders suggested that the MAPOD model could be used to identify fields not complying with the required threshold or to identify areas in which testing would be required.
Stakeholders highlighted the fact that the introduction of GM maize and the setting up of coexistence will lead to a decrease in the freedom for farmers to choose whether to sow GM or non-GM crops. This choice will depend partly on the management of collection (spatial or temporal organisation of collection silos). It will also depend partly on the choices made on neighbouring farms, particularly in Alsace, where farms are quite scattered, fields are small and the density of maize is high.
Table 3. Changes in practice for managing the coexistence between GM and non-GM maize
Change in practice Intended effect
At field scale
Sowing of a non-GM buffer zone (area of non-GM maize all around the GM field) Harvesting the discard width of a non-GM field separately
Increase in mean distances between GM and non-GM maize
Using different sowing dates for GM and non-GM maize
Using different precocities for GM and non- GM maize
Separation of flowering times
Tilling to destroy maize volunteers Destruction of maize volunteers, a potential source of admixture*
At farm scale: crop management Clustering GM and non-GM maize
Increase in the area of adjoining plots under each kind of maize and in mean isolation distances between GM and non-GM maize Increasing distances between GM and non-
GM maize
Moving other crops so as to isolate GM maize
Moving set-aside so as to isolate GM maize
Increase in mean isolation distances between GM and non-GM maize
Sowing GM maize in isolated positions Increase in barriers to pollen flow **
Cleaning sowing machines, combine harvesters and/or trailers between GM and non-GM maize
Clustering the sowing and/or harvesting of GM and non-GM maize crops
Investing in new machinery so that dedicated machines can be used for each kind of maize (GM and conventional)
Limitation of admixture due to machinery
At landscape scale (requiring co-operation between stakeholders) Agreements between neighbouring farmers
to plant GM and non-GM maize at different times
Agreements between neighbouring farmers to use GM and non-GM maize crops with different precocities
Separation of flowering times
Agreements between neighbouring farmers to cluster GM and non-GM maize
Agreements between neighbouring farmers to swap fields (temporally or definitely)
Increase in mean distances between fields Compensating neighbours for the
adventitious presence of GM maize in the non-GM harvest
No need to change practices
* Maize volunteers are considered as null in Alsatian climatic conditions and in the MAPOD model.
** Barriers to pollen flow are not yet taken into account in the MAPOD model.
II.2. Management of critical points
Current farming practices that could be changed to face up to the new constraints of coexistence have already been studied (Messéan el al., 2006). These changes to farming practices and their intended effects are summarised in Table 3 (Duquesne, 2005).
The measures for managing critical points could be distinguished into three types (i) measures taken before maize cultivation, to limit GM adventitious presence due to GM seeds and/or GM volunteers (ii) pre-sowing measures for decreasing GM pollen dispersal (spatial and temporal isolation of GM and non-GM maize: isolation distance, buffer zone, organisation into clusters, flowering time lag) and (iii) post-sowing measures to select fields or parts of fields of interest (discard zone, combine-harvester cleaning, post-harvest operations). We assessed the effect of each measure on the adventitious presence of GM maize in the non-GM harvest and its feasibility.
II.2.a. Measures for limiting GM volunteers and seeds
GM maize volunteers are considered to be a negligible source of admixture in Alsatian conditions. Volunteers from previous maize crops are rare due to the cold winter climate. In France, the maximum number of volunteers is around 100 plants per hectare on average (Meynard and Le Bail, 2001). Moreover, maize volunteers tend to be highly visible in the field as they appear between the rows. Ploughing before the planting of maize crops destroys maize volunteers. Only two of the 24 farmers questioned were growing maize with conservative tillage in single-crop rotation, to save time for other activities. These two farmers no longer own a plough and therefore cannot plough before planting maize.
Traces of GM seed in non-GM seed lots are a key source of GM adventitious presence in the non-GM grain harvest. They were considered in an additive manner in this study (see D7.2 Alsace). Alsace has been declared a GM-free zone, and Alsatian collecting firms have been responsible for analysing the purity rate of non-GM seed lots. The additional costs entailed have been met by these collecting firms.
II.2.b. Pre-sowing measures Isolation distance
Efficacy of isolation distance: The effect of isolation distance between GM and non-GM fields was assessed by ranking mean cross-pollination rates in Heiwiller (Figure 4) and Ensisheim (Figure 5), according to the various “emitting field x recipient field” pairs.
It is true that some of the results recorded in the tables are illogical. For instance, an increase of the isolation distance, other factors being equal, should correspond to a decrease in cross- pollination rate, but an increase was observed. This discrepancy was due to variability in the number of “emitting x recipient field” pairs between groups, with this number sometimes being too small for the accurate calculation of mean cross-pollination rates. Moreover, the shape of emitting and recipient fields was not taken into account in the design of the groups, and this factor has a marked impact on cross-pollination. There were too few replicates per group to mask this variability due to field shape. Detailed calculations (number of pairs in each group and coefficients of variation) are presented in the appendices (appendices 2 & 3).
In spite of these discrepancies, we are shown that the distance between GM and non-GM fields is a major factor in maize cross-pollination. Mean cross-pollination rates are always below 0.9% if fields are separated by a distance of more than 20m.
Figure 3. Relationship between maximum cross-pollination rate due to a single GM maize field and GM adventitious presence due to all GM fields
In each simulation, for each non-GM field, we compared the maximum cross-pollination rate due to a single GM field with the total GM adventitious presence due to all GM fields. All GM and non-GM maize plants were considered to flower simultaneously.
Figure 2. Proportion of the total number of “donor field x recipient field” pairs in which cross-fertilisation levels exceeded the defined thresholds, as a function of distance (in m)
— Heiwiller
Nonetheless, the impact of the distance between GM and non-GM crops depends strongly on the spatial layout of GM and non-GM fields as a function of prevailing wind direction and on areas under GM and non-GM crops. For isolation distances below 20 m, cross-pollination rates are about 50 to 60% lower in upwind situations than in downwind situations, regardless of the areas under GM and non-GM crops. Increases in recipient field area dilute the GM grain in the harvest and give greater protection against invading pollen, resulting in lower levels of cross-pollination. Nonetheless, the manipulation of these factors (GM and non-GM field areas, spatial layout of GM and non-GM fields) is not sufficient to ensure compliance with even high maximum thresholds for cross-pollination. By contrast, the use of limited isolation distances of 10 and 50 m ensure compliance with maximum thresholds for cross- pollination of 2.25% and 0.9%, respectively (Figure 2). Isolation distances of 50 m appear to be strategically useful, as they make it possible to respect maximum thresholds of 0.6% and 0.4% in most situations.
It should be borne in mind that these distances were calculated for a situation in which there is only one donor field. At landscape scale, several GM donor fields may contribute to the adventitious presence of GM maize in the harvest of non-GM fields. The total GM adventitious presence due to all fields is compared with that due to a single field in Heiwiller in figure 3. For 4.9% of the non-GM fields, none of the GM fields individually causes a cross- pollination rate above 0.9%, whereas the total adventitious GM presence due to all these fields combined exceeds 0.9%. Thus, decision-making rules based on simulations with a single GM maize field (that likely to give the highest cross-pollination rate) are associated with a risk of error of about 5%. For a 0.1% threshold, the risk of error is 8%. Moreover, this risk increases with the proportion of the total maize area planted with GM maize, with values of 0.5% for 10% of the total maize area under GM maize and 4.4% for 50% of the total maize area under GM maize.
Feasibility of the implementation of isolation distance: The distance between GM and non- GM fields can be increased by modifying the distribution of GM and non-GM maize in fields, with the total proportion of maize in the AUA remaining the same. Farmers considered this method to be complicated, as it would compete with other agronomic decision-making rules, including the concentration of maize fields around irrigation systems. A similar issue would arise if a GM variety was adopted to deal with a particular weed or disease problem, resulting in its distribution in the landscape as a function of the occurrence of the problem.
Nonetheless, it is possible to modify the distribution of maize fields, as already been done for waxy production (waxy maize varieties were preferentially sown in isolated areas). However, waxy maize production volumes are small and contracts between growers and collecting firms define the growing conditions. Modifications to the distribution of GM and non-GM maize fields would require co-operation between farms, due to the scattered nature of farms in Alsace. Each farmer has an average of 5 or 6 neighbouring farmers in Heiwiller and Ensisheim respectively (with fields located less than 5 metres from his fields). It is particularly difficult to increase the distance between GM and non-GM maize fields in Alsace due to the high density of maize (particularly in the Harth) and the small mean field area (particularly in the Sundgau).
Isolation distance could also be increased by decreasing maize density. In particular, other crops could be grown as a buffer between the two kinds of maize. Farmers are reluctant to decrease their use of maize, which is a profitable crop (in irrigated areas, other crops may not generate enough profit to pay for investment). Labour demands for maize production are low and maize cropping is compatible with part-time farming. Some farmers currently grow only maize. If they had to diversify their crop rotation to decrease the density of maize, they would have to invest in new machinery. Nonetheless, the Common Agricultural Policy now.
Figure 4. Mean cross-pollination rates calculated for each group of “emitting field x recipient field” pairs, classified according to their main characteristics – Heiwiller
Upwind situations
0-5 5-10 10-20 20-30 30-50 50-75 75-100 100- 150
150- 200
200- 300
300- 400 >400 0-1
1-2 2-5 5-9 0-1 1-2 2-5 5-9 0-1 1-2 2-5 5-9 0-1 1-2 2-5 5-9 Non-GM field
area (ha)
GM field area (ha)
2-5
5-9 0-1
1-2
Isolation distance (m)
Downwind situations
0-5 5-10 10-20 20-30 30-50 50-75 75-100 100- 150
150- 200
200- 300
300- 400 >400 0-1
1-2 2-5 5-9 0-1 1-2 2-5 5-9 0-1 1-2 2-5 5-9 0-1 1-2 2-5 5-9 Non-GM field
area (ha)
GM field area (ha)
Isolation distance (m)
1-2
2-5
5-9 0-1
The various “emitting field x recipient field” pairs were classified according to their characteristics (emitting and recipient field areas, distance between the two fields and the position of the emitting field with respect to the recipient field). The non-GM field is considered to be downwind if one of the prevailing wind directions involves the flow of air from the donor field to the recipient field. Based on the wind directions prevailing in the region (see D7.2 Alsace), downwind situations correspond to the pairs in which the donor field is located west, north- west or south-west of the recipient field. Upwind situations correspond to all the other layouts of GM and non- GM fields.
For each group of field pairs, we calculated the mean cross-pollination rate. These rates were classified into seven groups, shown in different colours (6 thresholds for cross-pollination: 0.01%, 0.1%, 0.4%, 0.6%, 0.9%
and 2.25%). The white squares represent groups with description criteria not corresponding to the characteristics of any pair of fields in the area studied.
Assumptions: AUA includes 50% maize — 10% and 50% of total maize area under GM maize. All GM and non- GM maize crops flowered simultaneously.
]0.6%,0.9%]
]0.4%,0.6%]
]0.1%,0.4%]
]0.01%,0.1%]
<0.01%
>2.25%
]0.9%,2.25%]
]0.1%,0.4%]
>2.25%
]0.01%,0.1%]
<0.01%
]0.9%,2.25%]
]0.6%,0.9%]
]0.4%,0.6%]
Figure 5. Mean cross-pollination rates calculated for each group of “emitting field x recipient field” pairs, classified according to their main characteristics – Ensisheim Upwind situations
0-5 5-20 20-50 50-100 100-200 200-300 300-400 >400 0-1
1-5 5-10 10-20 20-51 0-1 1-5 5-10 10-20 20-51 0-1 1-5 5-10 10-20 20-51 0-1 1-5 5-10 10-20 20-51 0-1 1-5 5-10 10-20 20-51
Distance (m)
0-1
1-5 Non-GM field
area (ha)
GM field area (ha)
5-10
10-20
20-51
Downwind situations
0-5 5-20 20-50 50-100 100-200 200-300 300-400 >400 0-1
1-5 5-10 10-20 20-51 0-1 1-5 5-10 10-20 20-51 0-1 1-5 5-10 10-20 20-51 0-1 1-5 5-10 10-20 20-51 0-1 1-5 5-10 10-20 20-51
Distance (m) Non-GM field
area (ha)
GM field area (ha)
10-20
20-51 0-1
1-5
5-10
The non-GM field was considered to be downwind if one of the prevailing wind directions involved a flow of air from the donor field to the recipient field. Based on the prevailing wind directions in the region (see D7.2 Alsace), downwind situations correspond to field pairs in which the donor field is north, north-east, south, or south-west of the recipient field. Upwind situations correspond to all other layouts of GM and non-GM fields.
The white squares represent groups with description criteria not corresponding to the characteristics of any field pair in the area studied. All GM and non-GM maize crops flowered simultaneously.
Assumptions: 60% of AUA under maize — 10% and 50% of total maize area under GM maize. All GM and non- GM maize crops flowered simultaneously.
Table 4. Decrease in cross-pollination rate due to non-GM width in a situation with no discard width (from Messéan et al., 2007)
Wind situation Downwind Upwind
Perpendicular to the GM and no GM field
layout Non-GM width 9 m 12 m 18 m 9 m 12 m 18 m 9 m 12 m 18 m
0 m -35% -42% -50% -60% -65% -71% -45% -51% -59%
20 m -17% -21% -30% -21% -25% -35% -19% -24% -33%
50 m -14% -17% -24% -16% -20% -28% -15% -19% -27%
100 m -11% -14% -21% -12% -15% -22% -12% -15% -22%
Isolation distance
more than
150 m -9% -12% -18% -9% -12% -18% -9% -12% -18%
requires crop rotation (Council Regulation (EC) No 1782/2003). The implementation of these new regulations on crop diversity at farm scale may lead to a slight decrease in maize density.
During the first working group, stakeholders suggested that wheat could be grown as a substitute crop. Wheat also has the advantage of providing straw, which is useful for cattle farmers. However, wheat yields are less steady than those for maize and wheat cultivation is more time-consuming.
Non-GM buffer zones
Efficacy of non-GM buffer zones: The effect of non-GM buffer zones was simulated in a field-scale study (Messéan et al., 2006), which showed that non-GM buffer zones had little impact on cross-pollination rate (Table 4). The effect of non-GM buffer zones decreases sharply with increasing isolation distance. Indeed, the effect of a non-GM buffer zone is mainly due to an increase in isolation distance, whereas such zones seem to provide little protection against GM pollen. Little difference was found between the effect of an isolation distance of 20 m and a non-GM width of 18 m. Nonetheless, Messéan et al. (2006) showed that the use of a non-GM buffer zone makes it possible to comply with a 0.9% purity threshold with no isolation distance if the areas of GM and non-GM fields are similar.
Feasibility of the implementation of non-GM buffer zones: The use of a non-GM buffer zone results in some non-GM maize being mixed with GM maize in the GM collecting silo. It implies that there is no non-GM purity rate requirement for GM maize. In the working group, farmers said that it was easy to set up a non-GM buffer zone encompassing the first rows at the edge of the field. Indeed, farmers are used to sowing strips of maize at the edge of the field, and then sowing the bulk of the crop in rows along the length of the field. The width of this strip at the edge of the field is quite variable (from 8 rows to 24 rows), depending on field area and machinery (width of the pulveriser). Most of the farmers questioned (14 of 18) felt that the sowing of a strip of non-GM maize was feasible, as it did not involved a major change from current practice. The only change involved is that the seeds used must be changed between the non-GM strip and the centre of the field, which is planted with GM maize. The sowing of a non-GM buffer zone in a GM field would also be more time-consuming for agricultural work contractors. However, given the variability of strip width, this measure may not always be sufficient. Field area is a major constraint, as farmers may not be able to increase the width of the strip without the centre of the field becoming too small. Some farmers pointed out that these two kinds of maize might need different treatments, increasing the workload.
Clustering of GM and non-GM maize
Efficacy of clustering GM and non-GM maize: The clustering of maize crops of the same variety is commonly used in seed production. The Swiss case study (see D7.2 Swiss) showed that organising non-GM production into clusters can lead to a 90% decrease in the mean cross-pollination rate evaluated at landscape scale. This study concluded that this decrease resulted mostly from an overall increase in isolation distance and that pollen protection from neighbouring non-GM maize fields was limited.
Feasibility of clustering GM and non-GM maize: It is difficult to cluster GM and non-GM maize fields on individual farms in Alsace, as most farms are highly dispersed. It also goes against other decision-making rules for crop allocation, as described above. Instead, farmers must jointly decide, on a voluntary basis, to cluster GM and non-GM crops in a specific area.
Figure 6. Proportion of the total number of “donor field x recipient field” pairs with cross-pollination results exceeding defined thresholds, as a function of flowering time lag
Flowering time lag = difference (in days) between the onset of flowering in non-GM and GM crops
Figure 7. Effect of flowering time lag at landscape scale
Among the simulations implemented in the first part of this study (see D7.2 Alsace), 3 GM and non-GM maize distributions in fields were selected. They are represented by a cross in the boxplot of results without flowering delay. For each simulation, several distributions of flowering dates were used.
This organisation could be combined with the spatial organisation of silos. However, stakeholders pointed out that it is quite difficult to organise GM and non-GM maize production into clusters in Alsace because farms are scattered and field areas are small.
Moreover, the clustering of GM and non-GM maize makes it possible to achieve a high purity rate, but there is still coexistence at the limits of the clusters. Thus, only a small number of farmers, at the limits of clusters, have to deal with coexistence issues. This raises questions about the division of costs between those involved in the cluster
Flowering time-lag
Efficacy of flowering time-lag: The proportion of field pairs for which the non-GM harvest is downgraded seems to follow a normal distribution as a function of flowering time lag (Figure 7). This proportion is particularly low if the flowering periods of the GM and non-GM fields are asynchronous, lowering the proportion of non-GM silks that can be pollinated by GM pollen, which depends on period of overlap between non-GM female flowering and GM male flowering. This proportion is highest if flowering time lag is negative (between -2 and -6 days). This may be because we simulated an average protandry of one day. Thus, in each field, there is one day when no pollen is emitted but the silks are receptive and can be pollinated by pollen from other fields, including GM pollen.
At the landscape scale, the mean cross-pollination rate with a natural flowering time lag is, on average, lower than the mean cross pollination rate with no such time lag (Figure 8).
However, the results are quite variable with respect to the spatial distribution of flowering dates. The maximum mean cross pollination rate with a natural flowering time lag is similar to that with a synchronous flowering period. The variability of the results due to distribution of flowering period is lower than that due to the distribution of GM and non-GM maize in the field pattern. The flowering of non-GM fields after GM fields results in a decrease in cross- pollination rate (median decrease of 25% and 50% for time lags of 2 and 4 days, respectively – Figure 9). However, the box plots overlap, indicating that the distribution of flowering periods within the field pattern is important. The mean cross-pollination rate does not decrease if the non-GM maize flowers before the GM maize. It is due to protandry: at the end of the non-GM flowering period, non-GM silks are receptive but only GM pollen is emitted.
Feasibility of the implementation of flowering time lag: The flowering times of GM and non- GM maize can be separated by using several varieties, some flowering earlier than others, or by sowing on different dates. Stakeholders did not deal with flowering time lag during the first working group. This measure was discussed in the survey. The mean range of sowing dates per farmer was 14 days, varying from 7 to 20 days. Decision-making rules for the choice of sowing date and variety precocity are presented in D7.1. At field scale, sowing date depends on the meteorological conditions and the type of soil. Sowing takes place later in the Sundgau than elsewhere in Alsace, because the heavy, clayey soils of this area require longer to dry out. At farm scale, sowing period and the choice of a precocity variety depend on the contribution of maize to farm income, labour availability and competition between activities.
It is clearly more difficult to separate GM and non-GM flowering periods for the farmers who currently use a concentrated sowing period. Maize is the principal crop on some farms: the objective here is maximising yields. Once agronomic constraints have been taken into account, this results in the farmer sowing the latest flowering varieties as early as possible. Yields are higher for later flowering varieties. For these farmers, time isolation measures (later sowing date or earlier flowering variety) represent a loss of profit. Some farmers prefer to concentrate their sowing activities because they are involved in other activities and lack time. For instance, one of the farmers interviewed was also an agricultural
Figure 8. Effect of different decision-making rules on the distribution of flowering dates over time
Table 5. Decrease in cross-pollination rate due to discard width for a situation with no isolation distance between GM and non-GM fields (from Messéan et al., 2006)
Estimate of decrease in cross-pollination rate Wind situation
6 m 12 m 24 m
Downwind - 27% - 37% - 49%
Upwind - 50% - 62% - 71%
Perpendicular to the GM and non-GM fields layout
- 35% - 46% - 58%
