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Growing burdens? Disease-resistant genetically modified bananas and the potential gendered implications for labor in Uganda

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Abstract

How will the adoption of genetically modified (GM) staple crops reconfigure labor processes in Sub-Saharan Africa? This article focuses on Uganda, where GM varieties of matooke (cooking bananas), the country’s primary carbohydrate staple, are expected to be commercialized within the next few years. The paper draws on survey data and focus groups with a random sample of over one hundred and fifty growers to investigate the potential ways a variety engineered to be resistant to banana bacterial wilt (BBW) might impact labor dynamics. A BBW resistant GM variety will displace labor currently allocated to disease prevention and control, and increase the labor required for harvesting higher yields. How farmers can address the need for more harvesting labor varies significantly according to region. In the southwestern highlands, producers can increase their portion of hired labor. In the central and eastern regions, where farms tend to be smaller and subsistence-oriented, farmers are more likely to intensify their use of unpaid family labor, particularly that of wives. Hence, while GM technology may result in increased yields for small-scale farmers in Uganda, this is likely to come at the cost of intensifying the agricultural labor burdens of women in the central and eastern region.

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Notes

  1. What makes BBW particularly challenging is that all cultivars of matooke appear vulnerable, and there is no known treatment; once a banana plant is infected the whole plant needs to be destroyed. Affected plants develop symptoms within a month of infection: if contamination occurs before flowering the initial symptom is a progressive yellowing and wilting of the leaves, if contamination occurs after flowering the first sign tends to be a shrivelling and wilting of the male bud (Tripathi et al. 2009). In either case the result is premature ripening, leading to blotchy and rotted fruit. Eventually, the leaves, fruit and stem all rot completely and the plant collapses, usually from the top downwards. Infection can be spread by insect vectors such as fruit flies and bees, though experts believe that the most prevalent spread is due to contaminated tools, especially machetes (panga) which are used on infected plants and then used again on healthy ones (Mwangi and Nakato 2009).

  2. The NARO official made this comment in an interview on November 5, 2013.

  3. In comparison, few studies have considered the labor and gender impacts of GM food crops. Exceptions include GM maize (Gouse 2012; Reiger et al. 2013), rice (Huang et al. 2005), soy (Trigo and Cap 2003) eggplant (Francisco 2006) and papaya (Gonsalves et al. 2007). With the exception of Gonsalves et al. (2007)—who argues there will be no change in labor practices with GM papaya—these studies argue that the technology will prove labor saving, and hence more profitable, though none explicitly consider how gender relations will be affected.

  4. This sampling figure reflects the geographical distribution of matooke farmers in Uganda. According to the Ugandan Census of Agriculture for 2008/2009, the country’s matooke farmers are split unevenly between the three growing regions, with 15 % of total located in the east, 35 % of total located in the central, and 50 % of total located in the southwest.

  5. The majority of our focus groups were held separately with men and women. In a few cases we combined men and women.

  6. A pearson Chi square test shows that region significantly shapes the hiring of workers χ2 (N = 167) = 27.24, p < .001.

  7. This figure was reported to us by farmers in focus groups in Isingiro and Ntungamo districts in the southwestern region.

  8. This income data is somewhat deceiving because one outlier in our data set earns 8 million USH per month (a large matooke farmer in the southwest). Overall, 87 % our sample is below 400,000 USH per month.

  9. Note that the monthly income of 400,000 USH is significantly higher than the GNI per capita for Uganda of 94730 USH in 2012 (UNICEF 2012 http://www.unicef.org/infobycountry/uganda_statistics.html).

  10. One farmer indicated that if government provided them with workers, it would then be able to claim some of their produce: “If the government provides us with labor, the output will go to the government, just like in Tanzania” Another farmer in Ntungamo thought government involvement might drive up wages even further: “If the government gives us labor, it will put up minimum wage, which they can afford.” Farmer fears associated with government intervention may also account for the low ranking given to accessing markets, since as we saw above many farmers are marginalized in this respect.

  11. According to a kruskal–Wallis test, farmers in the southwest are significantly more likely to rank “labor” a higher priority than the central (H = 17.06, p = 000), but not the eastern.

  12. Not all banana-producing households engage in all of these practices because of labor and resource constraints.

  13. While the main unit of analysis in our article has been “male-headed households,” the labor and gender impacts of a BBW resistant GM variety on female-headed households are also deleterious. Female-headed households are usually more constrained from hiring labor than their male headed counterparts (Beraho 2008), hence with added labor demand there would be little choice but to increase unpaid self and family labor.

Abbreviations

ADIS:

Agriculture development and investment strategy plan 2010/11–2014/15

BBW:

Banana bacterial wilt

Bt:

Bacillus thuringiensis

GM:

Genetically modified

IRIN:

UN office for the coordination of humanitarian affairs

NARO:

National Agricultural Research Organization

NERICA:

New rice for Africa

Ht:

Herbicide tolerant

USD:

United states dollars

USH:

Ugandan shillings

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Acknowledgments

Our primary thanks go to the farmers who gave up their time to share their insights with us. This research could not have been completed without the exemplary work of our Ugandan research team, Sarah Mujabi-Mujuzi, Tonnie Mirro, and Rodgers Atwooki. Sean P. Mackinnon (savvystatistics.com) was the statistical consultant for this paper. We greatly appreciate his assistance with the data analyses. We also appreciated the comments from two anonymous reviewers.

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  1. Department of Anthropology, Memorial University, 210 Prince Philip Drive, St. John’s, NL, A1C 5S7, Canada

    Lincoln Addison

  2. Department of International Development Studies, Dalhousie University, 6135 University Ave, Halifax, NS, B3H 4R2, Canada

    Matthew Schnurr

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Addison, L., Schnurr, M. Growing burdens? Disease-resistant genetically modified bananas and the potential gendered implications for labor in Uganda. Agric Hum Values 33, 967–978 (2016). https://doi.org/10.1007/s10460-015-9655-2

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