| Literature DB >> 26414379 |
John Herbert Ainembabazi1, Leena Tripathi2, Joseph Rusike3, Tahirou Abdoulaye4, Victor Manyong5.
Abstract
BACKGROUND: Credible empirical evidence is scanty on the social implications of genetically modified (GM) crops in Africa, especially on vegetatively propagated crops. Little is known about the future success of introducing GM technologies into staple crops such as bananas, which are widely produced and consumed in the Great Lakes Region of Africa (GLA). GM banana has a potential to control the destructive banana Xanthomonas wilt disease.Entities:
Mesh:
Year: 2015 PMID: 26414379 PMCID: PMC4587572 DOI: 10.1371/journal.pone.0138998
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Characteristics of respondents (percentages and means).
| Farmers | (N = 37) |
| % of smallholder farmers (versus progressive) | 64.9 |
| % of male respondents | 75.7 |
| Average age (years) | 55.4 |
| Education (years of schooling) | 7.4 |
| Experience in banana production (years) | 26.7 |
| % of crop income from bananas | 62.2 |
| Traders | (N = 47) |
| % of banana retail traders (versus wholesalers) | 61.7 |
| Experience in agricultural produce trade (years) | 14.1 |
| Experience in marketing of bananas (years) | 12.2 |
| Number of banana bunches sold on a market day | 86.3 |
| % of business income from banana trade | 57.1 |
| Agricultural extension agents | (N = 21) |
| % of extension agents working with government institutions | 33.3 |
| Experience in area of academic training (years) | 11.5 |
| Experience in banana extension work (years) | 10.1 |
| Key informants | (N = 7) |
| % of key informants working with government institutions | 71.4 |
| Experience in area of academic training (years) | 16.2 |
| Experience in banana extension work (years) | 14.6 |
Fig 1Farmers’ awareness of BXW disease in target countries.
Fig 2Losses in banana production due to BXW incidences in target countries.
Control methods for BXW and awareness of genetically modified (GM) crops.
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| |
| Removing the male bud | 10.8 |
| Removing infected plants | 21.6 |
| Uprooting whole infected mat | 18.9 |
| Avoiding introduction of suckers from unknown locations | 2.7 |
| Combination of control methods | 54.1 |
| % of respondents aware of the development of BXW resistant banana varieties (N = 111) | 27.9 |
| % of respondents aware of the meaning of GM banana (N = 75) | 36.0 |
|
| |
| a banana which has been bred to resist diseases | 34.5 |
| an improved banana with integrated gene(s) from other sources | 41.4 |
| a banana variety with different properties from local varieties (good eye appeal but tasteless, has long term health effects) | 24.1 |
|
| |
| Support the development | 83.0 |
| Indifferent | 3.6 |
| Do not support the development | 13.4 |
| % of respondents reporting potential benefits of GM banana resistant to BXW (N = 111) | 94.6 |
|
| |
| Increase in yields | 28.1 |
| Increase in income | 38.3 |
| Credit to research institutions | 3.7 |
| Stable supply | 12.4 |
| Increase in banana consumption | 17.5 |
| % respondents reporting potential disadvantages of GM banana resistant to BXW (N = 110) | 40.0 |
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| High cost of establishing new plantations | 24.0 |
| Undesirable consumption attributes | 14.8 |
| Outbreak of new diseases and loss of local varieties | 27.8 |
| Health problem concerns | 24.1 |
| Failure of GM banana to adapt to local conditions | 9.3 |
Note: In some cases, the number of observations is the sum all respondents in the study (farmers, traders, extension agents and key informants). The number of observations varies for some questions due to missing responses.
Fig 3Willingness of farmers to adopt GMB-BXW in target countries.
Farmers’ willingness to pay for and reasons for adoption of GMB-BXW.
| Reasons for early adoption of GMB-BXW (N = 33) (%) | |
| To limit the spread of BXW | 36.4 |
| To restore plantations destroyed by BXW | 36.4 |
| To improve yields and income | 24.2 |
| To be among early adopters | 3.0 |
| Reasons for late adoption of GMB-BXW (N = 9) (%) | |
| To limit the spread of BXW | 22.2 |
| Need to first learn from early adopters | 66.7 |
| Negative attitude to improved varieties | 11.1 |
| % of farmers willing to pay for GMB-BXW (N = 31) | 90.3 |
| Minimum price farmers are willing to pay (US$) | 0.71 |
| Maximum price farmers are willing to pay (US$) | 1.43 |
| Area expected to be allocated to GMB-BXW (acres) (N = 37) | 1.7 |
| % of respondents reporting to cut down existing banana plantation and replace with GMB-BXW (N = 26) | 26.9 |
| % of respondents reporting to establish new banana plantation by land re-allocation (N = 26) | 73.1 |
Note: The number of observations varies due to missing responses
Parameter values used to estimate the economic benefits of GMB-BXW using ESM.
| Country | Burundi | DRC | Kenya | Rwanda | Tanzania | Uganda |
|---|---|---|---|---|---|---|
| Price per ton (US$) | 267 | 255 | 198 | 205 | 203 | 207 |
| Research costs (million US$) | 3.2 | 3.2 | 3.2 | 3.2 | 3.2 | 3.2 |
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| Average loss of banana production due to BXW | 51 | 83 | 39 | 45.9 | 43.5 | 71.4 |
| Expected initial adoption rate of BXW resistant banana (%) | 30 | 70 | 20.9 | 48 | 24 | 35.3 |
| Expected ceiling of adoption rate of BXW resistant banana (%) | 65.5 | 100 | 60.4 | 70 | 77.5 | 73.8 |
| Expected number of years to attain maximum adoption | 3–5 | 3–5 | 5–10 | 2–5 | 5–10 | 5–10 |
| Average % increase in area allocated to banana after adoption | 23.1 | 50 | 30.6 | 26 | 10.3 | 40 |
| Average % increase in input costs per ha after adoption | 27.5 | 50 | 28 | 35 | 20 | 33 |
| Average % yield gain after adoption per ha | 56.7 | 70 | 39.4 | 30 | 25 | 53.8 |
| % of banana production from study areas with respect to whole country | 15 | 13 | 27 | 38 | 33 | 28 |
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| Production (‘000 tonnes) | 1,184 | 832 | 1,424 | 3,220 | 3,260 | 9,770 |
| Banana area (ha) | 178 | 361 | 61 | 349 | 727 | 1,830 |
Data sources
1The average farm gate price from the 37 surveyed farmers who sold bananas.
2Research costs extracted from the GMB-BXW project proposal budget for Kenya and Uganda.
3Key informant survey data (from banana breeders, research scientists and extension agents). Key informants were asked to provide information on banana losses, potential adoption of GMB-BXW, and production by major regions producing bananas in their respective countries. The figures provided are average figures across regions in each country and the sample sizes vary per country.
4FAOSTAT for data for the year 2014, available on http://faostat.fao.org.
aNote that these variables were not used in the ESM, but are reported to provide additional explanation.
Fig 4Projected adoption rate of GM BXW resistant banana in target countries.
Fig 5Proportion of annual banana production sold.
Fig 6Farmers’ and consumers’ preferences and inquiries.
(A) Represents farmers’ consumption preference for banana varieties. (B) Represents consumers’ inquiry about banana before purchase from either farmers or traders.
Preferences and consumption attributes between improved and local banana varieties.
| Reasons for preferring local varieties to improved ones (N = 19) |
|
| Local varieties are tasty | 84.2 |
| Local varieties cook fast | 15.8 |
| Reasons for being indifferent between consuming local and improved varieties (N = 9) | |
| Both improved and local varieties are tasty | 55.6 |
| Both varieties cook fast | 44.4 |
| Attributes considered during purchase of bananas (N = 146) | |
| Quality and taste of banana | 37.0 |
| Size of the bunch and fingers | 50.7 |
| Banana prices | 5.5 |
| Good eye appeal | 5.5 |
| Long shelf life | 1.3 |
Fig 7Potential consumers of GM banana (N = 101) in target countries.
Fig 8Frequency (%) of reasons for consumer preference of GM banana.
Potential benefits of developing GMB-BXW in the GLA region.
| Country | Burundi | DRC | Kenya | Rwanda | Tanzania | Uganda |
|---|---|---|---|---|---|---|
| Adoption ceiling area (‘000 ha) | 117 | 361 | 36 | 244 | 567 | 1,354 |
| NPV of gross consumer surplus (millions US$) | 110 | 119 | 42 | 19 | 61 | 658 |
| NPV of gross producer surplus (millions US$) | 55 | 60 | 21 | 9 | 31 | 329 |
| NPV of net total benefits (millions US$) | 161 | 168 | 60 | 20 | 76 | 953 |
| Internal rate of return (%) | 56.48 | 57.79 | 43.21 | 29.6 | 43.14 | 85.64 |
| Benefit–Cost ratio | 33.85 | 17.13 | 20.71 | 3.62 | 6.11 | 30.05 |
Net Present Values (NPV) computed using a real interest rate of 10%.
Fig 9The upper panel reports the changes in NPV relative to baseline values (Table 6) due to doubling of inputs costs, reduction of initial and ceiling adoption rates by 50%, and reduction of yield by 25%.
The middle panel reports the changes in IRR relative to baseline values (Table 6) due to doubling of inputs costs, reduction of initial and ceiling adoption rates by 50%, and reduction of yield by 25%. The bottom panel reports the changes in BCR relative to baseline values (Table 6) due to doubling of inputs costs, reduction of initial and ceiling adoption rates by 50%, and reduction of yield by 25%. Notes: In the upper, the yield gain for Rwanda was reduced by 5% as reduction by 25% would lead to economic losses. In the middle panel, the IRR for Rwanda could not be computed when input costs are doubled, while IRR for Tanzania could not be computed when yield gain was reduced by 25%.