A modeling assessment of geneflow in smallholder agriculture in West Africa
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Small-scale agriculture is an important issue for food security in Africa. In the context of Genetically Modified Organisms, approaches to quantify geneflow in small-scale systems are widely unexplored. We aimed at bridging this gap by contributing to the scientific discussion on the uncertainties of the cultivation of genetically modified (GM) crops in the region. The safety issue is: Would it be possible to withdraw a variety in case that unexpected and undesirable effects occur? e.g. the resistance of pests which make the variety no more useful.
We used a GIS approach to determine the location of maize cultivation sites, field geometries and applied a model for the calculation of geneflow scenarios.
The data revealed that the given cropping density provides optimal conditions for transgene spread, potentially limiting the possibility for coexistence between GM and non-GM fields. On average, we found about 60 fields within a nearest distance of 100 m, and cropping density of 56 fields per square kilometer. The resulting cross-pollination rate from the single GM field into the neighbouring conventional fields was estimated to be about 0.12%.
GM varieties if introduced could remain in cultivation even if their admission has expired or has been retracted. This would be undesirable and could cause long-term, undesirable stacked combination of transgenes which cannot be tested with respect to eventual combinatory effects. These developments pose major challenges for fielder livelihoods, and conservation of maize genetic resources with potentially negative consequences for the African food export sector.
In spite of an obvious need, few studies exist focusing on biosafety research in Africa. This paper therefore presents an account of a project that assessed the implications of Genetically Modified Organisms (GMOs) in small-scale agricultural systems in Africa by focusing on a specific sector of agricultural food production in Ghana. Maize cultivation has been used in this instance to distinguish the differences that exist between agriculture in the USA or Europe, and elsewhere in other developed countries and those of the African conditions; in particular, looking at the agricultural structure, crop field locations, isolation distances between cultivated fields and spatial patterns of agricultural fields which are completely heterogeneous. On the basis of a modelling approach, representative scenarios are calculated to address the possible impacts of gene flow between genetically modified (GM) and conventional fields due to cross-pollination.
There are also concerns about the unclear nature of the use of genetic resources in the advent of GMO and related issues of patenting and biopiracy since small traditional fielders would like to benefit from their many years of sustaining seed biodiversity maintained over centuries . There is the fear of a high possibility of transgene escape if grain is used as seed, assumed likely under the present agricultural circumstances given the traditional seed exchange and utilization culture . These issues are discussed as legitimate concerns in this context since maize is widely used as food in Africa, with the crop representing the largest component of food for the greater segment of the African population.
It is also noteworthy that elsewhere in the world, the deployment of GM maize has practically caused widespread environmental, economic and legal problems . For example, there have been events of genetic contamination by transgenes in managed non-transgenic conventional production fields in Mexico . Again, GM pollen with insect resistance may pose potential hazard to non-target insect species as has been reported by several authors [13, 14, 15]. As far as regulation is concerned, it has been argued that though various acts and regulations are in place in some African countries and are supposed to be implemented, there is no formal system to verify the GM content of trans-boundary consignments, save for the permission of permits. The regulation of mandatory labelling of GMOs is inactive and there is no provision for GMO labelling in terms of consumer preference .
Owing to the aforementioned complexities of seed use or exchange practices, agricultural structure, increasing land use and maize cultivation intensity, weak regulatory and enforcement capacity in African countries, the safety issues refer to whether it would be feasible to recall a GM variety in case the unexpected happens. For example, with an occurrence of undesirable effects such as the resistance of pests which make the variety no longer useful.
Use geographic information system (GIS) to characterize crop fields to assess their distribution and isolation distances.
Conduct frequency and cropping density analysis to assess feasibility of coexistence measures.
Simulate regional cross-pollination to determine potential for geneflow in smallholder systems following a modeling approach.
We hypothesize that smaller cultivated fields and higher heterogeneity of the seed sources implicitly lead to an increased geneflow and increased genetic exchange in the longer term. This is a preliminary study in which minimal baseline scenarios have been used relevant for biosafety assessment for African agriculture taking into account an African environmental perspective.
Ground surface data based on GPS measurements
Specification of single locations of field allotments based on GPS readings of first point of entry of the cultivation area referred herein as field.
For estimating minimum distance between fields. This is an important parameter for estimating the probability of gene (pollen) transfer from genetically modified to conventional maize fields (or vice versa).
This is also useful to estimate the length of field borders.
Estimation of total acreage of fields - measurements taken at corners of the cultivation area ranging from 3-22 corners, depending on field extent.
Mean field size gives information on the dispersal characteristics of the cultivation area.
The spreading of pollen is more likely in regions with large number of smaller fields than in regions with fewer larger fields.
Specification of precise location points within same habitat patch.
For estimation of nearest neighbour relations.
Assesses the probability of cross-pollination between fields and feral locations
Five (5) scenarios were assessed, implying that genetic modifications (GM) or transgenes get introduced through mode of seed acquisition and via larger fields as follows:
― Scenario 1: GM seeds sown were obtained under exchange conditions, meaning that the seeds were obtained from other fielders as gifts or exchanged.
― Scenario II: GM seeds sown were obtained from the seed market. This directly implies the use of commercial GM varieties.
― Scenario III: A single GM field introduced at the center of the study area. This suggests the scenario of a single GM field among 1,388 conventional fields.
― Scenario IV: GM seeds sown were obtained from seeds saved from previous harvests.
― Scenarion V: GM seeds sown were obtained from food market. This scenario implies that variety planted was collected from quantities bought for food.
The model was run 10 times per scenario and average calculations written to an output file (see Table 4).
1. Cropping density and field geometry
Cropping density factors of maize fields in the study area
Cropping density (number of fields km-2)
Total maize area calculated from GIS records (km2)
Fractional area of maize as a % of total study area
% Field sizes below 0.5 ha
% Field sizes between 0.5-1 ha
% Field sizes between 1-2 ha
% Field sizes above 2 ha
Cropping isolation distances of field neighbours
Number of field neighbours within distance ranges
Mean (maximum number of fields )
2. Simulations of regional cross-pollination
Assessment of potential impacts of geneflow based on various seed sources using the MaMo
Average GM content in conventional seed harvest
GM fields created as a percentage of total number of fields (including conventional fields)
GM field area estimated as a percentage of total field area (including conventional fields)
Scenario I: GM planted was obtained under exchange conditions, meaning that the seeds were obtained from other fielders as gifts or exchanged.
Scenario II: GM planted were obtained the seed market. This directly implies the use of commercial varieties.
Scenario III: GM planted was obtained from seeds saved from previous harvests.
Scenario IV: Single GM field introduced at the center of the study area. This suggests the scenario of a single GM field among 1,388 fields
Scenario V: GM planted obtained from food market. This scenario implies that variety planted was collected from quantities bought for food.
The model provides average cross-pollination rates basing on several world-wide studies capturing the variability in climate and environmental factors. A map was derived for all locations where seeds had been planted from: (a) exchange sources, (b) seed market and (c) and those obtained from previous harvests. The single GM central field (d) had been assumed in order to derive hypothetical scenario for the possible impacts of a single GM field, and (e) assuming that GM seeds planted were obtained from food market. This scenario implies that variety planted was collected from quantities bought for food (see Table 4).
3. Model Scenario 4: single GM field in the centre of the study area
Modeling simulation with a single GM field located in the centre of the investigated region (circled). Each of the fields serves as a pollen source and calculates the impact to all other fields. It shows the involvement of random processes depending on size and location of fields as well as sowing time.
1. Agro-structure and coexistence considerations
The data show that the use of isolation distances between GM and conventional fields as a management measure or requirement to minimize or control gene flow is challenged in the given conditions. Most fields are small, with about 97% of fields below 0.5 ha (Table 2), occurring in very close proximity (Figure 3). For example, on a scale of 100 m, a maximum of three, four, five, seven and eight field neighbours would have to be expected at distances of 20, 40, 60, 80 and 100 m, respectively (Table 3). With a minimum nearest neighbour distance of 5 m and a maximum nearest distance of 459 m (Figure 3), the practice of co-existence of GM and conventional cropping would not be possible. In an event of GM introduction, on-field conservation of maize genetic resources is unlikely due to potentially higher cross-pollination in smaller fields (Figure 5). These findings coincide with studies conducted in Brazil  e.g. setting of a minimal isolation distance for coexistence for maize fields would be impractical. Hence, the usefulness of isolation distances under the given conditions is challenged. We conclude therefore that the hypothesis that for smaller cultivated fields and higher heterogeneity of the seed sources implicitly lead to increased geneflow and increased genetic exchange holds true.
2. Cross-pollination scenarios and implications for gene flow
the efficient regulation of maize grains used as food or feed products or even for seed imported into the country since it is highly unlikely to control transgene spread in the environment should they be later found out to be genetically modified varieties;
to consider the cost implications for small fielder livelihoods and the additional cost to the local seed biodiversity that must well be taken into account.
We conclude that GMO maize should not be cultivated within the agricultural systems in Ghana and other West African countries with comparable agricultural conditions and efforts to introduce them should be curtailed.
The authors would like to thank Mr. Christian Aden formerly of the Department of Landscape Ecology of the University of Vechta, Germany for support with the GIS approach.
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