In October 2017 the Ugandan Parliament passed the National Biosafety Act which granted the Uganda National Council for Science and Technology the authority to regulate the development and release of GM crops in Uganda. The bill, which has since been returned to parliament for revisions, marks a clear step towards commercialization of GM technology in the region.
Uganda’s experimental program with agricultural biotechnology is now one the largest in Africa, buoyed by significant investments in infrastructure, experimental capacity, and personnel training in the last decade, largely sourced from the United States Agency for International Development and the Bill and Melinda Gates Foundation (Schnurr, 2013). The hallmarks of Uganda’s experimental program are GM versions of the East African Highland Banana, known locally as matooke—the country’s primary carbohydrate staple. Matooke bananas are eaten before they are ripe; generally they are peeled, boiled, and then roasted over a fire to produce a thick mash. They provide an estimated 30% of Uganda’s daily caloric intake and occupies the greatest proportion (38%) of utilized agricultural land across the country (Kalyebara, Wood, & Abodi, 2007). The senior research scientists in charge of the National Agricultural Research Organisation’s banana experimental program predicts that GM matooke could be ready for commercialization as soon as 2018 (Interview with Research Scientist #2, May 18, 2014). Despite this momentum, there is still significant uncertainty about the impact of commercialization of GM crops among African farmers. A recent study by Schnurr and Addison (2017) aims to increase our understanding of which socioeconomic variables influence farmer decision-making around GM carbohydrate staple crops in Africa.
According to the 2008/09 Uganda Census of Agriculture, .6% of all matooke bananas produced in the country were located in three major growing regions—eastern, central, and south-western. These also represent three distinct agroecological zones. The country’s matooke farmers are split unevenly between the three growing regions, with 15% of all matooke farmers located in the east, 35% are located in the central region, and 50% are located in the southwest. Schnurr and Addison (2017) created a stratified random sample to generate a dataset that reflected this geographical distribution. They used a methodological approach that incorporates both quantitative and qualitative methods in order to measure farmer attitudes towards GM crops and their likelihood of adopting GM technology. Findings from this study suggest these technologies are more likely to be adopted by the heavily marketized farmers in the southwestern region, whereas the central region is most resistant. They hypothesize that a mix of historical and geographical factors contributes to these contrasting regional perspectives. Their findings also suggest that contrary to previous research that suggested poorer households are more likely to be interested in purchasing GM planting materials than wealthier ones (Edmeades and Smale, 2006), they found that larger farmers are more likely to adopt than smaller farmers. This finding regarding farm size—and by extension, wealth—likely reflects the possibility that larger farmers are more market-oriented than their smaller counter-parts, producing the bulk of their matooke for sale rather than home consumption. These results resonate with studies conducted elsewhere that similarly found that larger, more affluent farmers are better positioned and more willing to take on the risks associated with the adoption and implementation of new, improved varieties (Arechavala-Vargas, Díaz-Pérez, & Huerta-Ruvalcaba, 2007; Consmuller, Beckmann, & Petrick, 2010; Skevas, Kikulwe, Papadopoulou, Skevas, & Wesseler, 2012). They also found that membership in a farmers’ association, planting improved varieties, and existing relationships with extension agents all increase the likelihood of adoption.
According to Schnurr and Addison (2017), these results underscore the need for targeted policies that are geared toward meeting the needs of those farmers who lack these resources, and the potential risk that these most vulnerable farmers could miss out on the potential benefits associated with GM versions of African carbohydrate staple crops. More generally, these findings underline the need for agricultural policies and experimental programs that recognize the stratified and differentiated patterns of attitudes and adoption that will follow the commercialization of GM versions of African carbohydrate staple crops.
Arechavala-Vargas, R., Díaz-Pérez, C., & Huerta-Ruvalcaba, J.P.(2007). Genetically modified maize in Mexico: Varied responses to technology. In Proceedings from Atlanta conference on science, technology, and innovation policy 2007. Atlanta, GA.
Consmuller, N., Beckmann, V., & Petrick, M. (2010). An econometric analysis of regional adoption patterns of Bt maize in Germany. Agricultural Economics, 41(3-4), 275-284.
Edmeades, S., & Smale, M. (2006). A trait-based model of the potential demand for a genetically engineered food crop in a developing country. Agricultural Economics, 35, 351-361.
Kalyebara, M.R., Wood, S., & A bodi, P.N. (2007). Assessing the potential impact of selected technologies on the banana industry in Uganda. In M. Smale & W.K. Tushemereirwe (Eds.), An economic assessment of banana genetic improvement and innovation in the Lake Victoria region of Uganda and Tanzania (Research Report No. 155, pp.141-153). Washington, DC: International Food Policy Research Institute (IFPRI).
Schnurr, M.A. (2013). Bio-hegemony and biotechnology in Uganda: Unraveling the strategies used to promote genetically modified crops into new African markets. Journal of Peasant Studies, 40(4), 639-658.
Schnurr, M.A., & Addison, L. (2017). Which variables influence farmer adoption of genetically modified orphan crops? Measuring attitudes and intentions to adopt GM matooke banana in Uganda. AgBioForum, 20(2), 133-147.
Skevas, T., & Wesseler, J. (2009). Coping with ex-ante regulations for planting Bt maize: The Portuguese experience. AgBioForum, 12 (1), 60-69.
Skevas, T., Kikulwe, E.M., Papadopoulou, H., Skevas, I., & Wesseler, J. (2012). Do European Union farmers reject genetically modified maize? Farmer preferences for genetically modified maize in Greece. AgBioForum, 15(3), 242-256.