You are here: Home / Research / Research Blog / 12/23/14 - "Social Seed Networks: Identifying Central Farmers for Equitable Seed Access" by Vincent Ricciardi

12/23/14 - "Social Seed Networks: Identifying Central Farmers for Equitable Seed Access" by Vincent Ricciardi

Social Seed Networks:  Identifying Central Farmers for Equitable Seed Access

by Vincent Ricciardi

Vincent Ricciardi, MSc, Geography, received an ARC research grant in Fall 2012 for his project entitled "Participatory Approach to Sustainable Technologies for Orange and Purple Sweetpotatoes (STOPS) in Northern and Upper East regions of Ghana".

Full article: 

Ricciardi, Vincent. 2014. Social seed networks: identifying central farmers for equitable seed access. Submitted to Agricultural Systems.

Background:

Global food insecurity has never been as prevalent as today. About one-sixth of the developing world’s population lacks access to the sufficient foods needed to maintain healthy, productive lifestyles (FAO 2012). Notably, Sub-Saharan Africa has among the most extreme percentages of food-insecure people, remaining around 33 to 35 percent since 1970 (Mwaniki 2006). Crop biodiversity has decreased 75 percent worldwide since the turn of the 20th century (FAO 2012) and is a leading reason for rural food insecurity in the Global South (Pautasso et al. 2012). Brush (1991) defines ‘agrobiodiversity’ as crop biodiversity that acts as the cohesive social and ecological life-support system - it sustainably supplies food access through promoting healthy topsoil, clean water, air, and carbon sinks. The majority of agrobiodiversity is comprised of farmer varieties that have been bred to thrive in regionally-specific agroecological conditions, make up the majority of subsistence farmers' annual seed grown, and provide dependable seed access of nutrient-rich varieties (Lehesranta et al. 2005; Raigón et al. 2008). Importantly, these farmer varieties are open-pollinated and can be saved for repeated use in upcoming seasons, dramatically reducing overall input costs for resource-poor farmers (Sallah et al. 2007; Tripp and Mensah-Bonsu 2013). Agrobiodiversity decline has been further exacerbated by decreases in landholdings, intensification of farming practices, and introduction of closed-pollinated modern varieties, which cannot be saved each year (Bellon et al. 2011; Pautasso et al. 2012).

Traditionally, agrobiodiversity is maintained by informal seed systems, in which subsistence-based farmers engage in social contracts with one another, exchanging seeds to ensure they have enough to plant each season (Almekinders and Louwaars 1999; McGuire 2007; Louwaars and De Boef 2012). Social relationships, such as kinship alliances, facilitate seed and information exchanges necessary for continual and equal seed access (Badstue et al. 2006; Abay et al. 2011). When there are insufficient yields, farmers rely on their social network to resupply depleted seed stores (Almekinders and Elings 2001). These informal exchange systems have been found resilient to certain levels of ecological shock, such as erratic weather patterns, and social stressors, such as poor quality hybrid varieties that are increasingly replacing traditional varieties to meet market demands (De Boef et al. 2010; Tripp and Mensah-Bonsu 2013). However, as subsistence farmers transition from a barter system to a cash-based economy the magnitude of market-oriented agriculture development of hybrid varieties becomes a leading cause for the decline of traditional varieties (Cromwell and van Oosterhout 2000).

Problem:

Community-based agrobiodiversity management projects have been at the forefront of integrating informal seed systems into food security initiatives. These projects co-create open-pollinated crops and reintroduce lost varieties. A prevailing critique regarding these projects is that there is unequal distribution of seed to communities - quality seed, when introduced, is not reaching target populations.

Purpose:

The purpose of this study is to identify key farmers who can distribute open-pollinated seed through their existing, diverse social channels. Data was collected and combined from two subsistence-based communities in Northern Ghana. Data includes information across all exchanged crops in the communities in order to understand full social networks of seed exchange. Trust has been a key finding in past studies, indicating farmers are more likely to go to their established exchange channels for new varieties and species of seed (Pautasso et al. 2012). Accounting for an entire seed network accounts for farmers’ built relationships.

Method:

This study combines social seed network analysis and regression to allow projects to generalize their results. The identification of central, well-connected famers is crucial for such projects to effectively reach as many producers with the smallest amount of resources. Centrality measures the degree in which an individual is connected to others in his/her network through relationships, experiences, or exchanges of goods and information (Borgatti et al. 2013). Multiple types of centrality measures have been proposed to study informal seed systems (Abay et al. 2011). For instance, ‘degree centrality,’ relates to the number of individuals with whom a single farmer exchanges crop seed. Hence, if a farmer trades seed with four other farmers, the degree centrality score is four. Degree centrality is overly simplistic when determining seed exchange flows for open pollinated seed across multiple growing seasons. ‘Harmonic closeness centrality,’ on the other hand, is a comprehensive measure that identifies individuals who are connected to the highest proportion of other nodes via multiple steps. To understand which types of farmers have the most equitable and efficient seed distribution networks, this study uses harmonic closeness centrality as a dependent variable in an ordinary least squares regression. Additional control variables were wealth, kinship alliances, gender, and geographic location.

Results:

Results suggest that harmonic closeness centrality can best be predicted across the study communities by identifying farmers who are geographically centrally located, who have well connected kinship networks, and who are male farmers. While distance and kinship have been identified in non-network studies as essential to seed exchange, this study maps how these two factors affect farmers’ access to quality, open pollinated seed. A critical finding in this study is that males are more central than females, yet have less frequency of seed exchanges. Projects using this method to inform their seed distribution efforts need to consider this gendered power dynamic where the female farmers are responsible for seed saving and exchange activities, while male farmers control access to local seed varieties. These findings validate the utility of social network analysis in unfolding the socioecological complexity of informal seed systems and offer an equitable approach to (re)introducing open-pollinated varieties. Please refer to below figures and forthcoming publication for more detail on results.

Tables:

Table 1-1: Descriptive statistics

Table 1-1 Descriptive statistics.png

Table 1‑3: Regression output

Table 1‑3 Regression output.png

Figures

Figures.png

Figure 5: The number of times crop seed was exchanged by variety across in Aduyuli and Diani’s seed exchange networks.

Figure 5 The number of times crop seed was exchanged by variety across in Aduyuli and Diani’s seed exchange networks.png

Figure 1: Geographic distance between seed exchange partners in Aduyuli; the axis represent latitude and longitude to depict the geographic layout of the communities. Cluster of nodes at the same geographic position represent households with multiple farmers engaged in seed exchange flows. Light grey nodes represents low wealth farmers, darker grey represents medium wealth farmers, and black represents wealthier farmers. Square nodes represent female farmers and circle nodes represent male farmers. The size of the node indicates the farmers level of harmonic closeness centrality. The lines’ widths indicate the amount of seed exchanged between farmers, while the lines’ arrows shows directionality.

Figure 1.png

Figure 2: Geographic distance between seed exchange partners in Diani; the axis represent latitude and longitude to depict the geographic layout of the communities. Cluster of nodes at the same geographic position represent households with multiple farmers engaged in seed exchange flows. Light grey nodes represents low wealth farmers, darker grey represents medium wealth farmers, and black represents wealthier farmers. Square nodes represent female farmers and circle nodes represent male farmers. The size of the node indicates the farmers level of harmonic closeness centrality. The lines’ widths indicate the amount of seed exchanged between farmers, while the lines’ arrows shows directionality.

Figure 2.png

Figure 3: Social distance between seed exchange partners in Aduyuli. Nodal layout is based on the Fruchterman and Reingold (1991) spring embedded algorithm, which lays out each node randomly then uses enables nodes to push and pull one another to find an optimum solution where there is minimal amount of stress on each spring as it connects with the entire network. This algorithm effectively places farmers with higher harmonic closeness centrality towards the center of the sociogram. The size of the node indicates the farmers level of harmonic closeness centrality. The lines’ widths indicate the amount of seed exchanged between farmers, while the lines’ arrows shows directionality. Light grey nodes represents low wealth farmers, darker grey represents medium wealth farmers, and black represents wealthier farmers. Square nodes represent female farmers and circle nodes represent male farmers.

Figure 3.png

Figure 4: Social distance between seed exchange partners in Diani. Nodal layout is based on the Fruchterman and Reingold (1991) spring embedded algorithm, which lays out each node randomly then uses enables nodes to push and pull one another to find an optimum solution where there is minimal amount of stress on each spring as it connects with the entire network. This algorithm effectively places farmers with higher harmonic closeness centrality towards the center of the sociogram. The size of the node indicates the farmers level of harmonic closeness centrality. The lines’ widths indicate the amount of seed exchanged between farmers, while the lines’ arrows shows directionality. Light grey nodes represents low wealth farmers, darker grey represents medium wealth farmers, and black represents wealthier farmers. Square nodes represent female farmers and circle nodes represent male farmers.

Conclusion:

Social seed network analysis also reveals that the social makeup of informal seed systems can be detailed, replicated, and used to promote more accessible community-based agrobiodiversity initiatives. The network approach not only echoes that agrobiodiversity goes beyond crop biodiversity and that it is perpetuated and comprised of cohesive social and ecological relationships that sustainably supply food access (Brush 1991). Careful attention needs to be taken when community-based agrobiodiversity development projects start to interact with informal seed systems. Each system has unique characteristics and social network analysis only captures specified relationships, seed exchanges, at one point in time. Other factors and relationships, which may be dynamic over time, most likely affect how farmers continually access seed and disseminate crops to other members in their community. While social seed network analysis makes informal seed systems visible to the researcher, the involved members typically implicitly understand these systems because seed exchange is an integral part of their daily livelihoods (Badstue et al. 2006). Future studies should focus on when and how to make participants aware of their own informal seed access and steps the community can take to make more equal seed distribution possible.

References:

Bellon, M. R., D. Hodson, and J. Hellin. 2011. Assessing the vulnerability of traditional maize seed systems in Mexico to climate change. Proceedings of the National Academy of Sciences 108(33): 13432-13437.

Abay, F., W.S. De Boef, and Å. Bjørnstad. 2011. Network analysis of barley seed flows in Tigray, Ethiopia: supporting the design of strategies that contribute to on-farm management of plant genetic resources. Plant genetic resources 9(4): 495.

Almekinders, C. J. M., and A. Elings. 2001. Collaboration of farmers and breeders: Participatory crop improvement in perspective. Euphytica 122(3): 425-438.

Almekinders, C.J.M., and N.P. Louwaars. 1999. Farmers’ seed production: new approaches and practices. London: Intermediate Technology Publications.

Badstue, L.B., M. R. Bellon, J. Berthaud, X. Juárez, I.M. Rosas, A.M. Solano, and A. Ramírez. 2006. Examining the role of collective action in an informal seed system: a case study from the central valleys of Oaxaca, Mexico. Human ecology 34(2): 249-273.

Bellon, M. R., D. Hodson, and J. Hellin. 2011. Assessing the vulnerability of traditional maize seed systems in Mexico to climate change. Proceedings of the National Academy of Sciences 108(33): 13432-13437.

Borgatti, S.P., M.G. Everett, and L.C. Freeman. 2002. UCINET 6 for Windows: Software for Social Analysis. Harvard, MA: Analytic Technologies.

Borgatti, S.P., G.M. Everett, and C.J. Johnson. 2013. Analyzing Social Networks. Thousand Oaks, CA: Sage.

Brush, S.B. 1991. A farmer-based approach to conserving crop germplasm. Ecology and botany45(2): 153-165.

Cromwell, E., and S. van Oosterhout. 2000. On-farm Conservation of Crop Diversity: Policy and Institutional Lessons from Zimbabwe. In Genes in the Field: On-farm Conservation of Crop Diversity, ed. S.B. Brush, 217-238. Rome, Italy: IPGRI; Ottawa, Canada: IDRC; Boca Raton, FL: Lewis Publishers. Frankel.

De Boef, W.S., H. Dempewolf, J.M. Byakweli, and J.M. Engels. 2010. Integrating genetic resource conservation and sustainable development into strategies to increase the robustness of seed systems. Journal of sustainable agriculture 34(5): 504-531.

FAO. 2012. The State of Food Insecurity in the World. Food and Agricultural Organization of the United Nations. United Nations. Rome, Italy.

Lehesranta, S.J., H.V. Davies, L.V. Shepherd, N. Nunan, J.W. McNicol, S. Auriola, and S.O. Kärenlampi. 2005. Comparison of tuber proteomes of potato varieties, landraces, and genetically modified lines. Plant physiology 138(3): 1690-1699.

Louwaars, N.P., and W.S. De Boef. 2012. Integrated seed sector development in Africa: a conceptual framework for creating coherence between practices, programs, and policies. Journal crop improvement 26:39–59.

McGuire, S.J. 2007. Vulnerability in farmer seed systems: farmer practices for coping with seed insecurity for sorghum in Eastern Ethiopia. Economic botany 61(3): 221–222.

Mwaniki, A. 2006. Achieving food security in Africa: Challenges and issues. UN Office of the Special Advisor on Africa (OSAA).

Pautasso, M., G. Aistara, A. Barnaud, S. Caillon, P. Clouvel, O.T. Coomes, and S. Tramontini. 2012. Seed exchange networks for agrobiodiversity conservation. A review. Agronomy for sustainable development 33(1): 151-175.

Raigón, M.D., J. Prohens, J.E. Muñoz-Falcón, and F. Nuez. 2008. Comparison of eggplant landraces and commercial varieties for fruit content of phenolics, minerals, dry matter and protein. Journal of food composition and analysis 21(5): 370-376.

Sallah, P., S. Twumasi-Afriyie, K. Ahenkora, K. Asiedu, K. Obeng-Antwi, S. Osie-Yeboah, P. Frimpong-Manso, A. Ankomah, and B. Dzah. 2007. Agronomic potentials of quality protein maize hybrids developed in Ghana. Ghana journal of agricultural science 40: 81-89.

Tripp, R., and A. Mensah-Bonsu. 2013. Ghana’s Commercial Seed Sector. IFPRI. Chicago.