Journal of
Agricultural Extension and Rural Development

  • Abbreviation: J. Agric. Ext. Rural Dev
  • Language: English
  • ISSN: 2141-2170
  • DOI: 10.5897/JAERD
  • Start Year: 2009
  • Published Articles: 455

Full Length Research Paper

The nexus between the interaction among Cowpea actors and its market shares in Uganda. A case of Oyam District

Tobby Akecha
  • Tobby Akecha
  • College of Agriculture and Environmental Sciences, Makerere University, Kampala Uganda.
  • Google Scholar
Prossy Isubikalu
  • Prossy Isubikalu
  • College of Agriculture and Environmental Sciences, Makerere University, Kampala Uganda.
  • Google Scholar
Losira. N. Sanya
  • Losira. N. Sanya
  • College of Agriculture and Environmental Sciences, Makerere University, Kampala Uganda.
  • Google Scholar
Naris Mubangizi
  • Naris Mubangizi
  • College of Agriculture and Environmental Sciences, Makerere University, Kampala Uganda.
  • Google Scholar
Jacob Agea G
  • Jacob Agea G
  • College of Agriculture and Environmental Sciences, Makerere University, Kampala Uganda.
  • Google Scholar
Marus Eton
  • Marus Eton
  • Faculty of Economics and Management Sciences, Kabale University, Kabale Uganda.
  • Google Scholar

  •  Received: 24 May 2022
  •  Accepted: 19 July 2022
  •  Published: 31 July 2022


With the increased use of technology in agriculture, new improved farming and varieties of seeds have been adopted. The study explored how the interaction among the actors helped in sharing vital information on market shares of improved cowpea and relative to improved beans. A cross-sectional research design was used, while quantitative and qualitative approaches were adopted. The respondents were purposively selected because of their expert knowledge in the study. The findings showed, that beans and cowpea enterprise had 0.23 (23%), and 0.14 (14%) interaction density, while 23 and 16 actors participated in the promotion of beans and cowpea enterprises respectively. The findings also show that as a cowpea entrepreneur a farmer is the most influential and dominant actor in providing and using information while in the bean’s enterprise, extension service providers stood out as the most influential actor and the farmers remain the most dominant actor. Fellow farmers showed higher influence and dominance in providing and sharing information and other services regarding cowpea. Cowpea was a more profitable and viable enterprise. The study recommends that researchers and other actors should engage in the promotion of improved technologies. There should be increased interaction amongst actors and this creates product loyalty and promotes channel distribution. The actors should also provide and share relevant and timely information regarding agronomic, post-harvest practices, potential market opportunities, and other services.

Key words: Actor’s interaction, innovation systems, improved technology, market shares, improved cowpea.


Improved agricultural technologies are seen as one way in which agricultural production systems contribute to addressing the challenge of food, nutrition, national economy, and income insecurity in  developing  countries (Loevinsohn et al., 2013). Notably, the challenge of the increased global human population of the 20th Century, with more than 7.5 billion inhabitants, and is expected to rise to 9.5 billion by 2050  continues  to  put  pressure  on the land use and challenge the agricultural production systems (FAO, 2012; Kikuru et al., 2013; Robert, 2016; Chenchen et al., 2019; Dwyer et al., 2012; Kikuru et al., 2013). Notwithstanding, agriculture remains a main source of income for about 2.5 billion people in developing countries (Challa, 2013; Mwangi and Karuiki, 2015). Considerably, for technologies to contribute to increased food production, income, and national economy, there is a need for consistent interaction among actors engaged in improved technology development and promotion (Lavison, 2013; UBOS, 2019; MFPED, 2019).

Correspondingly, actors’ interaction is viewed by authors differently, with, Bambang et al. (2013); Mukebezi et al. (2016) referring to information sharing among different departments in an organization to enhance management decisions. Relatedly, Matende and Ogao (2013) views actors’ interaction as users’ participation in the production system. Similarly, Carsten and Jari (2014); Widyarini et al. (2016) relates the interaction among actors to the engagement of elements like material flow, cash flow, information flow, and humans. Given their definitions, the study considered actors’ interaction as the intensity to which actors involved in the cowpea enterprise get in touch directly or indirectly to provide or share required information and other services (agronomic and post-harvest practices, and potential market opportunities) to improve on cowpea production and market shares. Notably, information remains key in understanding the improved technology, in terms of agronomic, post-harvest practices, and potential market opportunities (Sally and Mcguire, 2016), to enhance market shares and profitability of the improved cowpea.

Relatedly, Latruff (2010) equates market shares as the ability of the improved technology to compete with existing technologies and successfully attain the market position. Similarly, market shares of a farm are linked to its presence in the market and generating future results of meeting strategic objectives (Molgorzata, 2016). On the other hand, market shares of the agriculture products are referred to as increased market gains (Darren et al., 2019), and lower costs of production. Accordingly, to cowpea enterprise, market shares are considered as the volume in kgs of improved cowpea produced and sold, relative to improved beans (NABE 2), in a growing season (2019-2020), and profit gained from sales of improved cowpea (SECOW 4W and SECOW 3B), relative to improved beans (NABE2 and NABE4), in a growing season.

As such, cowpea as an improved technology was used in the context to understand such interaction and how it supports increased market shares and profit. The study focused on how and why actors engaged in cowpea enterprise interacts, in a bid to enhance marketing and profitability, supported by UBOS, (2019) who alluded, that for the rural farmers to survive in the existing competitive market, access and  use  of  relevant  market  information (certified input source/price, competitive output price) needs to be consistent and adequate. Relatedly, frequent interaction between farmers and other actors creates product loyalty, since farmers are able to produce for a specific market, and customers (other actors) constitute part of the production and distribution processes (Rugema et al., 2017). Additionally, such interaction enhances collective marketing, wider market access, and collective action (Coulibaly et al., 2009; Adewale et al., 2014; Rugema et al., 2017). Conversely, the lack of synergies between actors the in agricultural value chain in Uganda may remain a major obstacle to the creation of market opportunities, increased market shares, and achievements of market gains by increased profits (Van Rooyen et al., 2017).  

Agricultural sector in Uganda

Agricultural sector is recognized as an important sector for national development in Uganda since it accounts for over 68% of the total employment, 28% of the Gross Domestic Product (GDP), and 48% of the export sector respectively (UBOS, 2018). The frame-works such as National Agricultural Policy (NAP) 2013, Seed and Plant Act (2006), National Agricultural Extension Policy (2018), Vision 2040, Uganda National Council of Science and Technology (UNCST), National Development Plan I, II, III, African Agenda, 2030-2063, East African Community, vision 2050,  all emphasize the need to enhance the competitiveness of the agricultural sector in terms of increased yields, market shares, and profitability as a key avenue to achieve the aspired middle-income status by 2040 (UBOS, 2019).  These frameworks advocate for the strengthening of the National Agricultural Research Systems (NARS), improved technology development, and extension services delivery systems to develop and promote improved agricultural technologies. (MFPED, 2019) reveals that if technologies are adopted, it may support improvement in crop varieties/livestock breeds and farm practices and extension services as a channel of technology dissemination.

Cowpea and interaction among actors in Uganda

The previous studies on the improved cowpea enterprise in Uganda focused majorly on improving breeds with related agronomic and posts-harvest practices (Bisikwa, 2014; Ayaa et al., 2018), in order to improve on the yields.  In the late 1960s, Makerere University Agricultural Research Institute Kabanyoro (MUARIK) in collaboration with Rockefeller Foundation, McKnight Foundation and later with research partners from National Semi-Arid Resources Research Institute (NaSARRI), developed and released several improved cowpeas including SECOW series,  1st  Tan (1T),  2nd  White (2W), 3rd B (3Black), 4thW (4White), 5th T(5Tan) and EBOWO series like ACCWC28, ACCW35b, ACCWC39, ACCNE 44. These varieties were tested for both scientific and socio-economic fit (Ddungu et al., 2015), and significantly the SECOW series were taken to the selected farmers by a team of researchers from NaSARRI and Ngetta ZADI through on-farm trials in Lango sub-region, Northern Uganda.

The interaction between researchers and selected farmers was meant to popularise improved cowpea among farmers by use of best agronomic and post-harvest management practices, although little is known about the contribution of researcher-farmer nature of interaction in enhancing the market shares and profitability of the improved cowpea relative to improved beans (NABE4). Beans are considered in the study for its being a leading legume food crop in the sub-region (MAAIF, 2019). However, considering the perspective of Agricultural Innovations Systems, cowpea actors including researchers, extension agents, farmers, Agro input/output traders, NGOs and others were meant to frequently provide and share information and other services beyond agronomic and post-harvest practices which would widen market opportunities of the improved cowpea. Bongiwe et al. (2014); Labarthe and Laurent (2013); Katie and Abigal (2014); Fossen and Schweidel (2016) posits that such interactions among actors provides exposure to potential new markets opportunities and create customer loyalty, since farmers are able to produce for a specific market and consumers constitutes part of the production and distribution channel (Katia et al., 2019).

Ayaa et al. (2018) notes that limited interactions with other actors could explain why farm yields, market shares and profitability of improved cowpea has stagnated at 250-450kg/ha, and cowpea being ranked 4th among the legume food crops respectively.  Guy et al. (2015) alludes to the notion that limited interaction among potatoe project farmers in Peru, Ethiopia, Uganda, limited farmers’ access to information, technologies, markets and services. The low ranking of cowpea among legume food crops by actors is probably due to low market shares and profitability and yet the crop (cowpeas), unlike other legume food crops adapts easily to harsh climatic condition and it’s one of the main sources of food eaten both as grain and green vegetable. As a contribution to the food security in the region, cowpea leaves can be dried and preserved for more than a year and additionally provides a cheap source of protein urgently required among the rural population of Northern Uganda.

UBOS (2019) reveal that there is a prevailing level of malnutrition in the region (Northern Uganda), with 37% of the population among under five years old experiencing staunted growth and yet such children would requires cheap and easily accessible source of protein like Cowpes and the interaction among cowpea actors would contribute towards improved yields, market shares and profitability, as a mechanism to provide a cheap source of protein among the vulnerable young population and reduce the income and food gap. The researcher’s previous proposition held that, frequency and intensity of interaction among actors in the cowpea enterprise positively influence the market shares of improved cowpea relative to the improved beans in the Lango sub-region. The frequent interaction between farmers and other actors creates product loyalty since actors operate as collaborators than competitors (Guy et al., 2015; Matende et al., 2013; Zaugg, 2016). The study provided more understanding on the actors, how cowpea actors interact as a pre-requisite in enhancing the market shares and profit of the improved technologies (cowpea). The improved market shares may facilitate the attainment of the anticipated NDP III, Vision 2040, NAP, 2013, NAEP, 2018, and other regional frameworks like, East African Community vision 2050, African Agenda, 2030-2063 and SDGs.The framworks advocate for the socio-economic transformation of subsistence farmers through increased income and food.

Theoretical framework

The study applied Agricultural Innovations Systems (AIS) theory (Klerxk and Nettle, 2013) and theory of information flow (Ron, 2014) that provide illustrations and the insights on actors and how actors interact to provide and share technical information and other services. The cowpea in the context of this study is a product of an innovation developed by multiple actors including extension agents, agro input/output traders and researchers. In order for the interaction to be effective, it has to be frequent and intense for it to offer an opportunity for actors to share relevant and timely information/other services needed by the farmers in order to improve on production and market shares. Ron (2014) notes that based on the theory of information flow, information is best shared using information sharing channels like, on-farm demonstration, local radio, mobile phone SMS/phone calls, brochure, face-to-face and for information shared to be useful, it has to be provided at the time that a farmer needs and it should be able to address the existing problem and the farmer should be able to apply it in a bid to improve on production, market shares and profitability.


A cross sectional research design was applied while both quantitative and qualitative approaches were used in data collection. These approaches enabled the researcher to explain the in-depth aspects of actors’ interaction, market shares and profitability. The questionnaires constituted closed ended question on actors, information sharing, market shares of improved cowpea and profit gained. The study was conducted in the district of Oyam in the sub-county and parishes of Amukugungu, Okii and Anyomolyec. From a total population of 475 farmers, a sample of 210 chosen  using  Morgan and krejice (1970) and a simple random sampling was applied in the selection. The respondents were purposively selected as they constituted areas where improved cowpea research was piloted by MUARIK, NaSARRI and Ngatta ZADI between 2012-2014.

Qualitative data were collected from the 20 Key informants including, 6 researchers from MUARIK, Ngetta ZADI, and NaSARRI, 6 agro input/output traders, 4 government extension agents and 4 NGOs in the sub-county of Otwal. Three (3) FGDs comprising of 10 members were held, per parish, Members of the FGDs included cowpea farmers.

The questionnaire was uploaded in Ordinary Data Kit (ODK) software and administered to the selected respondents in the three parishes. ODK software allowed the researcher to collect data on line review and forward the collected data to the central server for purpose of data security and further data management. 

Data analysis

Qualitative data (actors, roles, information shared, channels used) were collected using KIIs and FGDs were entered into Ucinet software version 6 using NetDraw tool and network maps generated to enable the researcher to visualize Social Network among cowpea and beans actors Oyam district. In the SNA, actors are referred to as nodes while relationships are referred to as ties. Network density provides estimates on potential actors participating in a network, network maps indicate relationships, and measures of centrality illustrate actors’ level of influence dominance (Borgatti et al., 2014). The relationship among actors can be positive or negative, that is, existence or nonexistence, and this is referred to as binary (Borgatti et al., 2014).  Quantitative data were analyzed using Statistical Package for Scientific Analysis (STATA) computer program to generate inferential statistics. Descriptive Statistics was run using Pearson (r) correlation coefficient to demonstrate the level of relationships between the nature of interaction among cowpea farmers and other actors on the market shares of improved cowpea. The structural Equation Model (SEM) was applied to examine the influence of intensity of interaction (actors’ ties, interaction density, average interaction) among cowpea farmers and other actors on the market shares of improved cowpea as compared to improved beans. As such, it (SEM) tests for measurement errors, and estimation of latent (unobserved) variables including the socio-economic variable via observed variables like actors’ relationships and establishes a structure that tests for model fitness and analyses data generated from a sample size of more than 200 (Byrne, 2016). Therefore, the model coefficient (beta) estimated the linear increase in the market shares of improved cowpea against improved bean varieties (NABE2 and NABE4), for each unit increase in actors’ intensity in the Lango sub-region. SEM is a statistical model that integrates different multi-variate techniques into one model framework (Byrne, 2016).


The study findings in Table 1 revealed only 16 actors/nodes are engaged in supporting production and marketing in the cowpea enterprise relative to beans with 23 actors. Only 0.14 (14%) of potential cowpea actors are providing and sharing information and other services with one another relative to beans 0.23 (23%). The cowpea had total estimates of 34 ties or relationships while beans had estimates of 118 ties or relationships. The average interaction between cowpea farmers with other actors is at least 2 times in a season relative to beans enterprise with average interaction of at least 5 times. The interactions among beans and cowpea actors are further illustrated in the network map in Figures 1 and 2.

This cuts across the three parishes of Amukugungu, Anyomolyec, and Okii, in the Oyam District. The interaction provides an opportunity for sharing of information and other services in a bid to enhance production and marketing.

The actors in Figures 1 and 2 are categorized into the followings; researchers, comprised of cowpea/beans breeders and agronomists from MaRCCI, Ngetta ZADI, and NaSSARI, whose mandates include; developing the foundation seeds of improved cowpea and beans and disseminating the seeds to seed dealers, extension agents, and farmers. Similarly, the agro-input/output traders whose key roles include; dissemination of cowpea/beans seeds/inputs and the purchase of agro output respectively. The core roles of government extension agents include; the dissemination of information and other services on agronomic/ post-harvest practices, a potential market of cowpea/beans, and other input to the farmers.  Non-Governmental Organizations and farmer organizations like AFST, OCDP, WFP, Alito Joint farmer co-operative, and Canomonanino, share a common mandate to provide capacity building to traders, and farmers’ mobilization and also provide potential input/output market information in a bid to improve on the production and market shares.

The study findings in Table 2 show that the cowpea enterprise farmer is the most influential and dominant actor in providing and using information and other services  with the highest in-degrees (39) and lowest out-degrees (0.000) respectively. Meanwhile, Table 3 shows the bean’s enterprise, extension service providers stood out as the most influential actor with the highest in-degrees of 33, while farmers remain the most dominant (0.000) actor. The more times an influential actor provide and share information and other services, directly or indirectly, the more they are able to bring in their diversity in the network, close the information and other services gap.

In   determining   the   market   shares    and    level   of profitability in cowpea and beans enterprises as a measure of enterprise competitiveness, Table 4 shows that cowpea is a fairly profitable enterprise compared to beans and for any money spent in cowpea enterprise, a farmer earns 1.71% as net profit compared to beans which earns 1.42%. On average, cowpea’s overall price is higher in the Oyam district at 2830 Ush, relative to beans with an averagely lower price of 2582 Ush. However, much as a farmer harvests more quantity of beans  (360 kgs)  and  sells  191kgs in a farming season, compared to cowpea, and 196 (kgs) respectively, cowpea still stood out  as  one  of  the  most desirable enterprises among legume crops to the farmer for food and income.

Structural equation modelling

In the cowpea model, Figure 3, actors’ ties reported a significant structural (Observed) influence with a p-value of 0.000, and improvement in providing and sharing information and other services by actors in the cowpea enterprise and these therefore increases market shares by a co-efficient of 1.40476 kgs in a season. Whereas, the actors’ ties in the bean’s enterprise, Figure 4 showed a p-value of 0.008, implying increased networking among bean actors would fairly increase volume in sales of beans output by 1.0002kgs in a season.

Similarly, the cowpea model showed a significant relationship between unobserved factor, land area allocated to cowpea enterprise in the three parishes of Amukugungu, Okii and Anyomolyec, Oyam district and yields with a p-value of 0.000. Thus, an increase in land size (acres) allocated to cowpea enterprise would increase volume in kgs produced and sold and thus increased market shares by a co-efficient of 97.91387 kgs in a season. Relatedly, being a female attached crop showed a significant negative relationship on the market shares of cowpea with a p-value of 0.004, and accordingly, such perception towards cowpea reduces its (cowpea) market shares by a co-efficient of -0.1174906kgs/ha in a season.

The bean model does not show any significant relationship between structural characteristics of the interactions, unobserved variables and market shares. The results in the bean model indicate that maintaining other factors constant, interaction among actors in the bean enterprise coupled with other socio-economic factors were not noticed to significantly influence production and sales of bean in the district. Farmers grow beans for mainly food and partly as a source of income and the contribution of extension agents reported as a main actor in the bean value chain is not significant to amplify production and open market opportunities.


A strong tie amongst cowpea farmers provides opportunity for sharing of information and other services in a bid to enhance production and marketing. The more times cowpea farmers are influential and a dominant actor would therefore provide and share information and other services directly or indirectly with fellow farmers. The information sharing would bring in the local diversity in the network, use local knowledge regarding agronomic, post- harvest and market opportunities to solve an existing farming and market challenges (Bambang et al., 2013). Cowpea farmers are sure of fellow farmers as the main source of information on agronomic, post-harvest practices, reliable grains and seeds market. Farmers (cowpea) are sure of when to produce and where to sell their farm output. They eliminate sharing of farm profit along the supply chain caused by middle men traders, since they are highly dependent on one another. Non the less, such a relationship has denied both crops (cowpea and beans) break even, with both enterprises operating at a loss.

Accordingly, Lee et al. (2012); Adewale et al., (2014); Zaugg (2016); Kyei-Boahen et al. (2017) alluded to the fact, that strong actors tie brings actors’ collaboration other than the competition in the value chain, and they (actors) may constitute part of production and distribution channels. Having such a contribution in the value chain would support the actors to develop product loyalty and avoid middlemen (traders) and consequently contribute towards increased market shares and profitability. This would also explain the profit variations emerging from price differences in the cowpea and beans enterprises emerging from higher prices gained by cowpea farmers. Relatedly, challenges in the public extension service provider as an influential actor in the bean enterprise could explain the low profit gained by bean farmers, as alluded to by MAAIF, (2019).

In Uganda, agricultural extension services have remained uncoordinated with diverse players creating conflicting  messages while farmers are not provided with input on time and inconsistent knowledge and information dissemination (MFPED, 2019). The poor coordination by actors is easily exploited by individuals or self-seekers in the value chain including middlemen traders Bhardwaj et al.  (2011), who conceal critical market information and share profit meant for the farmer by up to 75%.

However, considering the perspective of AIS, and providing an account of the improved cowpea, actors in the cowpea value chain were meant to frequently provide and share required information on (agronomic, post-harvest, and potential market opportunities) and other services, in a bid to serve the interest of the farmer as the main user of the innovations.

Conversely, in the case of cowpea, inadequate interaction among researchers certified input traders, agro output traders, extension agents, NGOs, and farmers lowered the opportunity for cowpea farmers to access timely and adequate information on certified input, output market, and other services. Farmers (cowpea) had no option but to resort to fellow farmers as a source of information on farm-saved seeds. Other input and output markets remained sealed off from competitive output markets beyond fellow farmers. Accordingly, such interaction diminishes the aspirations of the National Agricultural Extension Policy (NAEP), 2018, NDP, III (2020),  which  advocates  for  the  engagement   of  both public/private actors, NGOs, and researchers, toward the realization of the desired agricultural socio-economic transformation (MAAIF, 2019). This corroborates with Cerna, (2013); Tatek; et al. (2015) who maintain, that the continuous engagement of local market agents by the farmers, as the main source of input and output, denies them (farmers) market expansion across national boundaries, and enhancement in farm profitability. 

Thus, an increase in land size (acres) allocated to cowpea enterprise would increase market shares by a co-efficient of 97.91387 kgs in a season.  This tallies with Ndambi et al. (2009) who alluded, that larger farms are better  performers   for  they  benefit  from  economies  of scale and competitive output markets. Larger cowpea farmers would benefit from free transport due to bulk sales, credit opportunities, and high negotiation opportunities.  Increased output market as such may increase farmers’ income base, and probably influence their (farmers’) decision to allocate more land towards cowpea farming in a bid to fulfill market demands, and attain higher income gains. Correspondingly, Alene et al. (2015) alluded, that farmers regularly allocate significant acreage of the land for an enterprise depending on farm gains such as food and income.

Notably, being a female attached crop makes cowpea regarded as an orphan  legume   crop.   Thus,   male counterparts render the crop (cowpea) limited attention in terms of agronomic and post-harvest management practices, and in consideration of the limited authority that African women hold towards family land resource allocation and use. This is supported by the World Bank Group, (2018); Ojo (2011); Wikipedia (2010) the majority of smallholder farmers, most especially women farmers in Africa do not have access to adequate land. Ultimately, keeping cowpea as a kitchen gardening legume crop with low yields quality and limited marketing resources. Conversely, low attention to the crop (cowpea) as such disregards its attributes as a cheap source of protein needed to combat malnutrition among the population of children in Northern Uganda. UBOS, (2019) alluded that malnutrition in Northern Uganda has a prevalence of 37%, with stunted growth among children below 5 years, and yet the crop (crop) has a shorter maturity period of 14 days and would render it a necessity to support food security and alternative income source in the dry spelt regime.


The study found out that an increase in land size (acres) allocated to cowpea enterprise would increase volume in kilograms produced and sold and therefore increases market shares in a season. An improvement in the information sharing and other services by actors in the cowpea enterprise increased market share in a season.  An increase in networking among bean actors would fairly increase the volume of sale output. The established interaction framework set by actors creates a solid production, product loyalty, distribution, and marketing structure for the farmers to produce and sell to a specific food chain market and gain more profit. The study also established that farmers only grow beans for food and partly as a source of income and the contribution of extension agents was not significant in amplifying production and open market opportunities. The Interaction with actors may become part of the production and distribution channels and this leads to the development of product loyalty, enabling farmers to produce and sell at right time in a competitive market in a bid to reduce the income and food gap.

The study, therefore, recommends a continuous interaction among actors to enhance the development and promotion of improved agricultural technologies through information sharing and other services. The actors should also provide and share relevant and timely information regarding agronomic, post-harvest practices, potential market opportunities, and other services. There should be the promotion of improved technologies and this could be done by consistently engaging with other actors including agro-input/ output traders, local and international markets, private and public extension agents, Non-Governmental   Organizations,   schools,  and  other farmers’ associations.


The authors have not declared any conflict of interests.


Adewale A, Pluwole F, Buruchara R, Nyamwaro S (2014). Integrated Agricultural Research for Development: From concept to practice: Forum for Agricultural Research in Africa (FARA), Accra.


Alene AD Manda J, Gardebroek C, Kassie M, Tembo G (2016). Adoption and impacts of sustainable agricultural practices on maize yields and incomes: Evidence from rural Zambia. Journal of agricultural economics 67(1):130-153.


Ayaa F, Alumai A, Dranzoa C (2018). Indigenous Knowledge Influences Cowpea among Small Holder Farmers in Northern Uganda. International referred index journal volume 6(1):1-5. ISSN: 2320-3145


Bambang PK, Simatupang TM, Utomo SP (2013). Actors Interaction in the ERP Implementation Literature, Business process management journal 21(2):222-249. ISSN:1463-7154.


Bisikwa J, Kawooya R, Ssebuliba JM, Ddungu SP, Biruma M, Okello DK (2014). Effects of plant density on the performance of local and elite cowpea in Eastern Uganda. African Journal of Applied Agricultural Sciences & Technology 6(4):28-41. 


Bongiwe D, Masuku M, Kirsten JF (2014). Factors affecting the Competitiveness of the Agribusiness Sector in Swaziland. Journal of agricultural Studies 2(1). ISSN: 2166-0379.


Borgatti SP, Brass DJ, Halgin DS (2014). Social Research: Confusion, Criticism, and Controversies, Research gate,



Byrne BM (2016). Structural Equation Modelling with Amos. Basic Concepts, Application and programming (3rd edition). New York:Routledge


Carsten J, Jari DJ (2014). Games as Actors. Interaction, Play Design and Actor Network Theory. International Journal of Advances in Intelligent Systems 7(3-4):412-422. ISSN:1942-2679


Cerna L (2013). The Nature of Policy Change and Implementation; A Review of different Theoretical approaches. Journal of public Administration Research and theory 5(2):145-174.


Challa M (2013). Determining factors and Impacts of Modern Agricultural technologies adoptions in West Wollega, Munich, GRIN Publishing GmbH 4(20). ISSN:2225-093X (online).


Chenchen R, Shen L, Van Grinsven H, Reis S, Jin S, Liu H, Gu B (2019). The impact of farm size on agricultural sustainability. Journal of Cleaner Production 220:357-367. 


Coulibaly O, Alene AD, Manyong V, Sanogo D, Abdoulaye T, Chianu J, Fatokun C, Kamara A, Tefera H, Boukar O (2009). Unpublished article. Situations and outlook for Cowpea and Soya bean in Sub-Saharan Africa.


Darren R, Yong M, Purvis B (2019). Three pillars of sustainability: in search of conceptual origins. Sustainability science 14(3):681-695. 


Ddungu SP, Ekere W, Bisikwa J, Kawooya R, Kalule O, Birungi M (2015). Marketing and market integration of Cowpea in Uganda. Journal of Development and Agricultural Economics 7(1):1-11. 


Dwyer J, Ilbery B, Kubinakova K, Buckwell A, Menadue H, Hart K, Knickel K, Mantino F, Erjavec E (2012). How to Improve the Sustainable Competitiveness and innovation of the European Union Agricultural Sector: Brussel: European Parliament? ISBN: 97-92-823-3784-4,



Food and Agriculture Organization (FAO) (2012). The State of Food and Agriculture (2012). ISBN: 978-92-5-107317-9,



Guy F, Davis KE, Ragasa C, Franzel S, Babu SC (2015). International Food Policy Research Institute (Discussion Paper).


Katia AF, Álvarez-Ávila MD, Hernández CF, Schwentesius RR, Figueroa-Sandoval B (2019). Farmers market actors, Analysis and attributes: A Bibliometric study. Journal of Sustainability 11(3):745, 


Kikuru GT, Gachanja P Obere A (2013). The impact of Population Change on Economic growth in Kenya. International Journal of Economics & management Sciences 2(6):43-60. 


Klerkx L, Nettle R (2013). Achievements and Challenges of Innovation co-production support initiatives in the Australian and Dutch Diary sector. A comparative study. Food policy 40:74-89. 


Kyei-Boahen S, Savala CE, Chikoye D, Abaidoo R (2017). Growth and yield responses of cowpea to inoculation and phosphorus fertilization in different environments. Frontiers in plant science 8:646.


Labarthe P, Laurent C (2013). Privatisation of agricultural extension services in E. U: Towards a lack of adequate knowledge for small scale farm. Food policy 38:240-252. 


Latruffe L (2010), "Competitiveness, Productivity and Efficiency in the Agricultural and Agri-Food Sectors", OECD Food, Agriculture and Fisheries, Working Papers No.30. 


Lavison R (2013). Factors influencing the adoption of organic fertilizers in vegetable production.


Lee H, Park HJ (2012). Testing the impact of message interactivity on relationship management and organizational reputation. Journal of public relations research 25(2):188-206.


Loevinsohn M, Sumberg J, Diagne A (2013). Under what circumstances and conditions does adoption of technology result into increased productivity? Protocol, London, EPP, Centre, Social Sciences Research Unit, Institute of Education, University of London. 


Ministry of Agriculture, Animal Industry and Fisheries (MAAIF) (2018). Agricultural Sector Strategic plan, ASSP 2015/2016 to2019/2020.



Matende S, Ogoa P (2013). Enterprise Resource Planning (ERP) System Implementation. A case for User Participation Procedia Technology 9:518-526. 


Ministry of Finance, Planning and Economic Development (MFPED) (2019). Agricultural annual report.



Molgorzata M (2016). Resource Based Factor of Competitiveness of Agricultural Enterprises. Management 20(1):368-381. ISSN:2429-9321. 


Mukebezi R, Obaa B, Kyazze FB (2016). Influence of Organizational Structure on Actors Interaction with community level innovation platforms in Eastern Uganda. Ruforum working document 14(2):313-318, ISSN: 1607-9345


Mwangi MN, Karuiki S (2015). Factors determining adoption of new Agricultural technology by Small holder farmers in developing countries. Journal of Economics and Sustainable Development 6(5). ISSN 2222-1700 (Paper) ISSN 2222-28555 (online)


National Agriculture Extension Policy (NAEP) (2018). The Ministry of Agriculture Animal Industry and Fisheries.



National Agriculture Policy (NAP) (2013). The Ministry of Agriculture Animal Industry and Fisheries.



Ndambi OA, Garcia O, Balikowa D, Kiconco D, Hermme TY, Latacz-Lohmann U (2008). Milk Production System in Central Uganda. A farm Economics Analysis. Tropical Animal Health and Production 40(4):269-279.


Ojo CO (2011). Analysis of Women's Accessibility to Resource use for Agricultural Production in Borno State, Nigeria. Unpublished Ph. D Thesis, Department of Agricultural Economics and Extension Services, University of Maiduguri.


Robert JW (2016). Population growth and its Implication on Global Security. American Journal of Economics and Sociology 75(4):980-1004.


Ron W (2014). The study of information flow: A personal journey. Safety Science 67:58-63.


Rugema SH.; Seguya H, Kibwika P, Yildiz F (2017). Information Quality, Sharing & Usage in Farmer Organization. A Case of Rice Value Chain in Bugiri and Luwero Districts, Uganda. Cogent Food & Agriculture 3(1):1-16, 


Sally S, Joan M (2017). Using Diffusion Theory to promote Universally Designed College Instruction in Accra. International journal of Teaching and Learning in Higher Education 29(1):119-128. ISSN: 1812-9129.


Tatek A (2015). Reconceptualizing Children's Agency as continuum and interdependence Social Sciences 8(3):81. 


Uganda Bureau of Statistics (UBOS) (2019). Uganda Census of Agriculture. 


Van Rooyen AF, Ramshaw P, Moyo M, Stirzaker R, Bjornlund H (2017). Theory and Application of Agricultural Innovation Platforms for Improved Irrigation Scheme Management in Southern Africa. International Journal of Water Resources Development 33(5):804-823. ISSN:0790-0627, 


World Bank Group (2018). Creating Markets, Creating Opportunities. 


Widyarini M, Simatuoang TM, Engelseth P (2016). Social Interaction and price Transmission in multi-Tier Food supply chains. Journal of Operations and supply chain management 9(1):110-128. 


Wikipedia (2010). Nigerian Economy.



Zaugg M (2016). Analysis to inform Library communication patterns within the Harold B.Lee Library. Unpublished Library Assessment conference.