Socio-economic differences between innovation platform participants and non-participants: the case of smallholder dairying in Zimbabwe

Benjamine Hanyani-Mlambo, Maxwell Mudhara, Kefasi Nyikahadzoi¹ and Paramu Mafongoya

School of Agricultural, Earth & Environmental Sciences; University of KwaZulu-Natal. Private Bag X01. Scottsville 3209 South Africa
bmlambo2010@gmail.com
¹ Centre for Applied Social Science, University of Zimbabwe, Harare, Zimbabwe

Abstract

The concept of innovation platforms as a strategy for enhancing technology development, the dissemination of innovations, and market participation has received much attention in recent times among researchers in Sub Saharan Africa. However, very little is written on the determinants of participation in smallholder dairy innovation platforms, particularly for Southern Africa. This paper investigates the socio-economic differences between participants and non-participants in smallholder dairy innovation platforms, based on results of a cross-sectional survey of 227 households in Rusitu and Gokwe smallholder dairy schemes in Zimbabwe.

Results indicated statistically significant differences between the groups with respect to experience in commercial dairying, agricultural training received, household size, availability and access to labour, the main source of household income, dairy herd size, and the number of lactating cows (p < 0.01). The study also established statistical significance in differences in asset ownership; dairy management systems (p < 0.01); overall Knowledge, Attitudes and Practices (KAP) scores (p < 0.01); and household food and nutrition security (p < 0.05). Insights from this study have critical implications for smallholder dairy research and advisory services. They suggest a need for improvements in the design of key support services for the provision of training, capacity building, technical backstopping services, enhancing commercial dairying experience and growing the dairy herds for smallholder dairy farmers.

Keywords: participation, socio-economic

Introduction

The concept of innovation platforms as a strategy for enhancing technology development, the dissemination of innovations, and market participation has received much attention in recent times among researchers in Sub Saharan Africa (Martey et al 2014). On the other hand, participation in innovation platforms by smallholder farmers also holds considerable potential for allowing access to niche markets, yielding better returns, and ensuring sustainable livelihoods for this historically marginalized sub-sector (Omiti et al 2009). Innovation platforms, which facilitate interaction amongst actors, coordination, technological and institutional innovation, social learning and adoption of improved practices (Makini et al 2013; Boogaard et al 2013), have ushered in new hope, enthusiasm and prospects for improved relevance, effectiveness, and tangible impact through agricultural interventions. The interest in innovation platforms is being further propelled by the realization that barriers to agricultural development are not only technological but also institutional (Flinterman et al 2012).

Constraints and challenges within the smallholder dairying sector in developing countries remain subjects of both academic and developmental debate, and as priority intervention areas. Such constraints and challenges include low genetic potential, prevalence of various animal diseases, inadequate feeds and feeding, poor animal management, and unfavourable climate (FAO 2014). Other common issues facing the smallholder dairy sector in developing countries are the lack of appropriate handling and processing facilities resulting in concerns over milk quality, limited market access, low and volatile prices paid to farmers, poor management practices among producers, logistical bottlenecks, limited opportunities for enhancing productivity and increasing domestic supply, and weak linkages between different actors along the dairy value chain (ICAE 2015). In Zimbabwe, the smallholder dairy sector is characterized by low productivity, restricted market participation, and viability challenges (Hanyani-Mlambo et al 1998; Kagoro and Chatiza 2012; Chamboko and Mwakiwa 2016). Hence, the need for the development and sustainable support for innovation platforms within this sector.

This paper focuses on smallholder dairying due to the multiple benefits that, when operating effectively and efficiently, the sector can provide to producers. Benefits include a daily, more reliable and substantial source of income, improved household food and nutrition security, both directly through increased economic access to food, and indirectly given that the primary product, milk, is a balanced and nutritious food (ICAE, 2015). Smallholder dairying can also be a vehicle for national and regional development as it creates employment for numerous previously marginalized producers, is a source of not just income but also savings, and is one of the few agricultural enterprises that can be developed under varying environments (Salazar et al 2016).

Literature has many narratives on socio-economic factors affecting market participation of smallholder farmers for a variety of agricultural commodities. Past studies have identified factors such as gender, marital status, farmers’ access to credit and extension, market information, distance to market, land size, infrastructure, and external source of income (Randela et al 2008, Hlongwane et al 2014, Gebremedhin et al 2015). Only a few publications explore determinants of smallholder farmer participation in dairy markets and in innovation platforms. These publications identified household size, gender, age, education, distance to market, ownership of transport, communication facilities, and the number of milking cows as significant determinants of milk market participation among smallholder farmers (Kuma et al 2013, Kuma et al 2014, Balirwa et al 2016, Tadesse et al 2016). On the other hand, and in addition to the above, research has also established farming experience, literacy levels, and household income as significant determinants of smallholder farmers’ participation in innovation platforms (Martey et al 2014, Akinmusola et al 2016).

However, very few studies have undertaken comprehensive analyses, with most studies having been based on cursory analysis. Where these studies have been conducted within Sub-Saharan Africa, they have largely been restricted to West and Eastern Africa, with none in Southern Africa. As such, very little is written on the determinants of participation in smallholder dairy innovation platforms, particularly for Southern Africa. This paper investigates the socio-economic differences between participants and non-participants in smallholder dairy innovation platforms. It transcends conventional analyses of household and farm characteristics by also examining dairy management systems; knowledge, attitudes and practices; and effectiveness of innovation platforms.

Conceptual framework

Innovation platforms are physical, virtual, or physico-virtual networks of stakeholders, which have been set up around a commodity or system of mutual interest to foster collaboration, partnership and mutual focus to generate innovation on the commodity or system (Adekunle and Fatunbi 2012). They are fora of entities that share a common interest and come together to solve problems and develop mutually beneficial solutions (Makini et al 2013). It has, in fact, been argued that a key element of innovation platforms is in identifying bottlenecks and opportunities in production, marketing and the policy environment, and to leverage innovation to address the identified constraints and take advantage of opportunities across the entire impact pathway (Nederlof et al 2011; BMGF 2013).

In this paper, innovation platform participants are conceptualized as a group of farmers that comprise smallholder dairy association members who produce and deliver milk to the collection centres for collective marketing purposes, while non-participants represent smallholder dairy association members who produce milk for side-marketing.

In assessing the socio-economic differences between participants and non-participants in smallholder dairy innovation platforms and determining the effectiveness of innovation platforms, this paper hinges analysis on an adapted innovation platforms framework (Hanyani-Mlambo et al 2017). The framework consists of five major components viz:: the necessary conditions (drivers) for effective innovation platforms, innovation platform processes including farmer segmentation and stakeholder participation, innovation platforms, parameters measuring the effectiveness of innovation platforms, and strategic impacts (improved technology adoption, increased productivity and improved sector viability)(See Figure 1).

Materials and methods

Research context

The study was conducted in Rusitu and Gokwe smallholder dairy schemes in Zimbabwe, as a cross-sectional survey in 2015. Rusitu Dairy Resettlement Scheme is located about 440 kilometres east of Harare in Manicaland Province and falls within latitude 20⁰ 02’ S and longitude 33⁰ 48’ E. The scheme is located in agro-ecological region I, characterized by high rainfall, low temperatures, well-drained soils and provides a perfect environment for dairying (SNV 2013). The Gokwe Smallholder Dairy Scheme, on the other hand, is located 338 kilometres west of Harare in the Midlands Province and falls within latitude 18⁰ 13’ S and longitude 28 ⁰ 56’ E. The scheme is located in agro-ecological regions III and IV characterized by low rainfall, fairly severe mid-season dry spells and is, therefore, marginal for dairying (SNV 2013). However, despite the contextual contrasts, smallholder dairying remains the major source of income in both communities. Whilst milk production is an individual household activity, market participation is driven by cooperatives in a context where smallholder dairy farmer associations facilitate producers’ link with both input and output markets.

Figure 1. Framework for assessing the effectiveness of IPs (Hanyani-Mlambo et al 2017).

Sampling methods

Multistage sampling, a complex form of cluster sampling , was adopted to guide sampling for the household questionnaire survey. Rusitu and Gokwe were purposively selected as the two research sites given their contrasting characteristics and representativeness of the generality of smallholder dairy schemes in Zimbabwe. At the second stage, smallholder dairy farmers in both Rusitu and Gokwe were stratified on the basis of their level of participation in dairy innovation platforms. The household was then used as the unit of sampling during the third and final stage of sampling. At this stage and within the strata, a probability sampling method was used as the basis for selecting households included in the survey. A total of 227 households were sampled for the study. Of these, 100 households (44.1%) actively participated in smallholder dairy innovation platforms, while the remaining 127 households (55.9%) were not.

Data collection

Field data collection adopted a phased approach and the concurrent use of literature reviews, key informant interviews, focus group discussions, and a structured household questionnaire survey. The use of numerous data collection methods was deliberate as a way of triangulating collected data for purposes of verification, validation and improving the reliability of collected data (Babbie et al. 2001; Wagner et al. 2012). A formal survey using a structured household questionnaire was used to collect data on household demographics, participation in innovation platforms, farm amenities and conditions, asset ownership, livestock numbers and dynamics, dairy production and marketing, crop production, household food security, livelihood-based coping strategies, as well as access to livestock technology, inputs and support services. The data collection instruments were pre-tested to ensure that the study generates accurate, consistent, dependable and reliable data.

Statistical analyses

Socio-economic data was analyzed using the IBM SPSS statistical software version 22. Data analysis focused on five sets of variables viz: household and farm characteristics; asset ownership; dairy management systems; knowledge, attitudes and practices; and household food and nutrition security. Descriptive statistics such as frequencies and cross-tabulations were then used to generalize about the sample population and the differences between participants and non-participants in smallholder dairy innovation platforms. Cross-tabulation was used to determine the association between these variables. The significance of the association was determined using the Pearson’s chi-square tests, while the significance of differences between the two farmer segments was tested using t-tests. In all cases, p values below 0.1 were taken as statistically significant.

Results

Household and Farm Characteristics

Socio-economic factors have been identified as key drivers in determining smallholder dairy farmers’ participation in milk markets (Kuma et al 2014; Balirwa et al 2016; Tadesse et al 2016) and in innovation platforms (Martey et al 2014; Akinmusola et al 2016; Gyau et al 2016), albeit with no specific focus on smallholder dairy innovation platforms. Differences in household and farm socio-economic characteristics between smallholder dairy innovation platform participants and non-participants were analyzed on the basis of age, education, experience in commercial smallholder dairying, household size, access to labour, farm size, arable land size and utilized area, dairy herd size, and distance from the Milk Collection Centre (MCC). An analysis of survey results, based on mean differences, indicated statistically significant differences for experience in commercial dairying, household size, the number of household males and females aged 16 – 64 years, dairy herd size, and the number of lactating cows (p < 0.01). The rest of the explored socio-economic variables, including age, education, farm size and the distance from the market were statistically insignificant, which was unanticipated given insights from desk studies (Table 1).

The survey results above entail that on average, smallholder dairy innovation platform participants had more experience in commercial smallholder dairying, relatively larger households, more effective labour, and bigger dairy herd sizes in addition to a greater number of lactating cows. Other household categorical data viz: agricultural training received and main source of household income, were determined to be significant at p < 0.01 using Chi-square tests. On the other hand, gender and the highest level of education attained by the household head were not significant. This further supports the thesis that formal education on its own, without technical backstopping through practical training and the provision of advisory services, is not effective in transforming attitudes, practical skills and practices at the grassroots level (UNESCO 2017).

Asset Ownership

An analysis of asset ownership, based especially on the ownership of productive agricultural implements by the sampled households, show the level of resource endowment, their capacity, and a measure of both socio-economic status and well-being (Langyintuo 2008). Asset ownership is also a determinant of a household’s resilience to climate change and vulnerability to short-term shocks such as animal disease, droughts and flooding. It has also been noted that the number and type of livestock owned by particular households and by individuals within households under review is essential information for characterizing them, just as this is also an essential variable for determining other key indicators such as livestock productivity and incomes (Njuki et al 2011).

Tests for differences in the proportions of households falling in different wealth categories between innovation platform participants and non-participants had a statistically significant Chi-square value (p < 0.01) (See Table 2).

The results show that there is a significant difference between the percentages of innovation platform participants and non-participants falling within different wealth categories, based on the number of working assets. This means that participants in smallholder dairy innovation platforms have more assets in general than non-participants. The reason could be the nature of commercial smallholder dairying which is capital intensive, hence innovation platform participants’ greater resource endowments. On the other hand, higher incomes generated by innovation platform participants (Hanyani-Mlambo et al 2017) could probably be transformed into assets as part of smallholder farmers’ reinvestments in dairying and risk management strategies.

Dairy Management Systems

Dairy management systems are characterized by a variety of resources that include, inter alia, herd size, arable and grazing land area, forage and feeding management systems, herd health management, breed improvement strategies, milking practices, and marketing channels utilized (Dantas et al 2016).

Statistically significant differences were established in the proportion of sampled households utilizing a particular dairy management system, forage and feeding system during the dry season, dairy breed (stock type), and extension contact (p < 0.01). There were also statistically significant differences between innovation platform participants and non-participants on the basis of the main forage and feeding system used in the wet season and the mode of milk transportation (p < 0.05)(See Table 3).

In general, results from Rusitu and Gokwe districts show that participants in smallholder dairy innovation platforms had a higher level of adoption of recommended dairy management innovations. A notable 75.9% of the smallholder dairy farmers participating in innovation platforms adopted zero grazing, compared to only 24.1% from the sample of non-participants who adopted the same innovation. Likewise, more smallholder dairy innovation platform participants (62.5%) adopted the use of silage and/or hay as a supplementary feed during the dry season, relied on pure breeds as their main stock type (66.7%), and had more regular contact with extension and advisory services (79.2%). Several factors explain this. The core issues, however, hinge on participants’ greater interaction with innovation platform stakeholders, stronger linkage mechanisms, more immense interdependency and coordination, and the sharing of experiences and exchange of information amongst IP participants.

Knowledge, Attitudes and Practices (KAP)

Knowledge, Attitudes and Practices (KAP) surveys are predominantly conducted to collect information on what is known, believed and done vis à vis specific issues (Wood and Tsu 2008). KAP surveys are thus designed to identify what people know or their knowhow (Knowledge), how they feel or their perceptions (Attitudes), and what they do in reality or compliance (Practices), hence their use in diagnostic studies and in gathering valuable insights for designing appropriate interventions (Kaliyaperumal 2004). In this paper, a KAP survey was conducted not just for comparing the socio-economic differences between innovation platform participants and non-participants, but also for evaluating the effectiveness of smallholder dairy innovation platforms.

In determining KAP scores, knowledge question responses were scored 1 for a “yes” and 0 for a “no”. Attitudes were measured on a Likert 5 type scale, with strongly agree, agree, neutral, disagree, and strongly disagree being scored 2, 1, 0, -1 and -2, respectively. A Likert type scale was also used on practices, with the responses (never, rarely, sometimes, frequently, and always) being scored 0, 1, 2, 3 and 4, respectively. The KAP scores were tested for normality of distribution using one-sample Kolmogorov-Smirnov test. On the other hand, median/mean KAP scores were compared among different farmer segments, i.e. IP participants and non-participants, using the Mann–Whitney. A p-value less than 0.1 is taken as statistically significant. The results are presented in Table 4.

Differences in the level of knowledge between IP participants and non-participants were statistically significant for knowledge on business orientation; housing, infrastructure and equipment; breed improvement; and fodder production, feeding and feed management (p < 0.01); identification and herd management (p < 0.05); and business ethics and social influences (p < 0.1). A divergence of attitudes was adjudicated as statistically significant for housing, infrastructure and equipment (p < 0.01); business orientation; identification and herd management; fodder production, feeding and feed management (p < 0.05); and breed improvement (p < 0.1).

Statistically significant differences were also established in the adoption of practices for business orientation; housing, infrastructure and equipment; identification and herd management; breed improvement; fodder production, feeding and feed management (p < 0.01); business ethics and social influences (p < 0.05); and animal health (p < 0.1). The overall KAP score was 161.05 for smallholder dairy innovation platform participants and 132.64 for non-participants, with the difference again determined as being statistically significant (p < 0.01). Non-parametric tests at 0.05 confidence level also confirmed that KAP distributions between IP participants and non-participants are not the same, entailing that KAP results are influenced by one’s participation in smallholder dairy IPs.

Differential access to support services such as training, capacity building initiatives and extension contact between the two farmer segments explains these results. In addition, contrary to literature that portray knowledge, attitudes and practices as part of an innovation adoption continuum (Röling 1988; Bolding et al 2003), results from the study also show that innovation platforms had the greatest influence on practices than the influence they had on knowledge and attitudes.

Household Food and Nutrition Security

Household food and nutrition security were assessed on the basis of three parameters viz: (i) Months of Adequate Household Food Provisioning (MAHFP), (ii) Food Consumption Score (FCS), and (iii) Household Dietary Diversity Score (HDDS). The MAHFP captures the combined effects of a range of interventions such as improved production, storage and increased household purchasing power (Bilinsky and Swindale 2010). The FCS is a food consumption indicator that is used as a proxy for its reflection of the quality of diets and is, therefore, used as a proxy indicator for nutrition (Njuki et al 2011). Food consumption indicators are designed to reflect the quantity and quality of people’s diet. The FCS is a measure of dietary diversity, food frequency and the relative nutritional importance of the food consumed. Using a 7-day recall period, information was collected on the variety and frequency of different foods and food groups consumed to calculate a weighted score and, based on this score, classify households as having poor, borderline or acceptable consumption.

On the other hand, the HDDS is a proxy indicator for food security and a measure of household food access. It is defined as the number of unique food types consumed over a 24 hour period. The HDDS serves as a good complement to the FCS, as it provides a fuller picture of households’ diets (Njuki et al 2011). Differences between the FCS and HDDS measures were statistically significant (p < 0.01), while differences between IP participants and non-participants for MAHFP were statistically significant (p < 0.05) (See Table 5).

In general, results from the study show that smallholder dairy innovation platform participants were food secure over a longer period of time, in addition to better nutrition security. Independent samples tests conducted for MAHFP, FCS and HDDS by district also showed that there was no impact of location, which further buttresses the findings of this study.

Discussion

Survey results revealed highly significant differences between smallholder dairy innovation platform participants and non-participants based on their experiences in commercial dairying, the agricultural training received, household size, availability and access to labour, the main source of household income, dairy herd sizes, and the number of lactating cows. The rest of the explored socio-economic variables, including gender, age, education, farm size and the distance from the market were statistically insignificant. The results corroborate results from other studies, and yet produced some results that diverged from the findings of mainstream literature. The results present new insights and a new discourse as discussed below.

Tadesse et al (2016) identified household size, the number of cross breed and local breed lactating cows, access to credit, and the distance from the market as the significant factors affecting dairy farmers’ participation in milk markets in southwest Ethiopia. In Ethiopia, Kuma et al (2013, 2014) identified the age of the household head, dairy farming experience, milk yield per day, milking cow ownership, and the size of the landholding as significant factors in determining milk market participation. In Uganda, gender, age, education, distance to the market, ownership of transport, and communication facilities (P < 0.01) had highly positive and significant impact on smallholder dairy farmers’ decisions to participate in milk markets (Balirwa et al 2016).

Whilst no studies have focused on socio-economic differences between smallholder dairy innovation platform participants and non-participants, a number of studies focused on innovation platforms of other agricultural commodities. In an assessment of the factors determining cocoa farmers’ participation in innovation platform activities in Nigeria, Akinmusola et al (2016) identified farmer experience and education as key determinants. Based on a survey of smallholder rice farmers in Northern Ghana, the age of the household head, household size, and household income significantly influenced the willingness to participate in multi-stakeholder innovation platforms (Martey et al 2014).

Boughton et al (2007) argue that markets can only stimulate wealth creation amongst those with the capacity to participate given production constraints and the costs of market participation. Using an asset-based approach to analyse the level of market participation for rural households in Mozambique, the authors established that poorer households have limited capacity to participate effectively and hence need interventions to build up either their private stocks of productive assets, or the public goods that support agricultural production and marketing. Njuki and Sanginga (2013), using insights from Kenya, Tanzania and Mozambique, established that women tended to face more challenges when compared to their male counterparts in accessing and benefiting from markets, notably formal markets. Identified challenges included, inter alia, limited mobility; time poverty; lack of access to assets that would facilitate their participation; and lack of access to market information. These insights support results from this study which show significant association between participation in innovation platforms and asset ownership, entailing that participants in smallholder dairy innovation platforms have more assets in general than non-participants.

Predominant dairy management systems for innovation platform participants entail a higher level of intensification (including the adoption of zero grazing), the use of silage and/or hay as supplementary feeds during the dry season, adoption of pure dairy breed and crosses, as well as greater extension contact. A number of past studies confirm these findings. Dantas et al (2016) used cluster analysis in identifying four different segments of dairy producers in Brazil, in a context where farmer education and management levels, influenced the rate of technology and innovation adoption. In Algeria, Kaouche-Adjlane et al (2015) characterised breeding dairy cattle systems into different groups of farms based on their structure and management systems. In Morocco, feeding strategies and economic efficiency were used to classify dairy cattle farming systems into different farm segments (Srairi and Kiade 2005). In Kenya, Mburu et al (2007), used cluster and discriminant analysis in categorising smallholder dairy farms into different innovation domains based on risk management strategies, level of household resources, technology adoption, dairy intensification, and their access to services and markets.

In Kenyan avocado innovation platforms, Gyau et al (2016) established that age, education, gender, perceptions on knowledge and improved technology influence farmers’ decision to participate in collective action. In a study in Africa’s Great Lakes Region, Mulema and Mazur (2016) established that active participation in innovation platforms is sustained by the desire to access new knowledge and skills, anticipated economic benefits (markets, income, and credit) and material incentives (agricultural inputs), while participation was restrained by a cocktail of factors that included unfulfilled expectations of tangible immediate benefits, a lack of understanding of the IP concept, lack of resources, and prior commitments. The results from these studies thus, to a large extent, support the paper’s findings that show statistically significant differences in the level of knowledge, attitudes and practices between IP participants and non-participants.

The household food and nutrition security results in this paper are comparable to, but better than, national statistical assessments, with a range of 58 – 76.1% of households at national level being food secure between 2013 – 2016, a proportion of 54 – 68% of households having acceptable diets between 2011 – 2016, and an HDDS score of between 5 – 7 for the last five years (ZimVAC 2014; 2016). Smallholder farmers' engagement in markets is acknowledged as being important for improved household food security and poverty reduction (FAO 2017). A socio-economic evaluation of farm households in Cambodia, using the endogenous switching model, also yielded insights that showed that farm households participating in markets enjoyed higher household dietary diversity scores, thus supporting the hypothesis that participation in markets results in positive effects on farm households’ food security (Seng 2016). A study of smallholder agricultural households in Papua New Guinea also established a highly significant association between the level of food and nutrition security, on one hand, and market participation, on the other (Wickramasinghe et al 2014).

Insights generated by this study have critical implications for smallholder dairy research and advisory services. These key support services should be designed to provide training, capacity building, technical support services, enhancing commercial dairying experience and growing the dairy herds for smallholder dairy farmers. There is also need for greater targeting of dairy innovations in pursuance of specific innovation domains that are defined by characteristics beyond the conventional demographic factors, to encompass other non-conventional socio-economic aspects such as asset endowment, dairy management systems, KAP levels, as well as household food and nutrition security.

Conclusions

• There are highly significant socio-economic differences between participants and non-participants in smallholder dairy innovation platforms, entailing that socio-economic factors influence smallholder dairy farmers’ participation in innovation platforms.
  
  
• Statistically significant factors include smallholder producers’ experiences in commercial dairying, the agricultural training received, household size, availability and access to labour, the main source of household income, dairy herd sizes, and the number of lactating cows.
  
  
• In addition to conventional demographic factors and farm characteristics, unconventional socio-economic factors analyses such as dairy management systems; knowledge, attitudes and practices; as well as household food and nutrition security can also be used as parameters to distinguish between innovation platform participants and non-participants.
  
  
• There is need for the crafting of appropriate policies and the implementation of relevant interventions that can be effective in enhancing technology development, dissemination of innovations, and market participation by smallholder dairy farmers.

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Received 28 May 2017; Accepted 15 June 2017; Published 1 August 2017

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