DETERMINANTS OF PARTICIPATION IN INNOVATION PLATFORMS AND ITS SUSTAINABILITY: A CASE STUDY OF SUB-SAHARAN AFRICA†

Sub-Saharan


INTRODUCTION
In Sub-Saharan Africa, particularly in West Africa, smallholder agricultural productivity is still poor in comparison to agricultural productivity in other regions, and the availability of food per person is relatively static (Pretty et al., 2011;Hounkonnou et al., 2012).An increasing corpus of literature acknowledges that African farmers lack opportunities and that, to connect them to improved services and achieve development goals, it is necessary to reform institutional structures beyond the farm level (Salami et al., 2017).Smallholder farmers in Africa work hard to increase their agricultural production, food security, and income.However, African smallholder farmers must constantly modify their production systems due to frequent and abrupt changes in their production conditions, which calls for continuous innovation (Nederlof et al., 2011;World Bank, 2012).The establishment of innovation platforms (IPs) is one method that can assist African farmers in keeping pace with this ongoing innovation process (Cullen et al., 2014).There has been a noticeable transition in recent years from technology-focused to system-oriented approaches to innovation (Klerkx et al., 2012;Schut et al., 2016).One illustration is the increased focus on the Agricultural Innovation Systems (AIS) idea, which reframes innovation as emerging through the interaction of numerous people.An AIS is described as a network of businesses, organizations, and people working to commercialize radical innovations for goods, processes, and organizational structures, as well as the institutions and laws that influence how various actors communicate, share, access, exchange, and use knowledge (Hall et al., 2006).According to this concept, innovation encompasses social and institutional changes as well as new technologies.
Research and development organizations in SSA are increasingly experimenting with Innovation Platforms (IPs) as a tool to promote innovation by offering a social space for learning, experimentation, and negotiation among stakeholders as they work to operationalize the AIS concept (Schut et al., 2016).An IP is a place for learning and change, according to ILRI (2012).There is a diverse set of people involved-farmers, traders, food processors, researchers, government officials, etc.-who frequently represent the views of organizations.The members gather to diagnose issues, spot opportunities, and devise strategies for achieving their objectives.An IP is described in this paper as a setting where numerous actors or stakeholders with important interests can come together to solve issues through social learning.It emphasizes linkages between actors, knowledge flows, and incentive mechanisms.This paradigm holds that innovation of all types, whether technical or institutional, follows a non-linear process, and that the 'system' capacity depends on the 'density and quality of relationships' between the innovation-producing and using individuals, as well as supporting institutions (Altenburg et al., 2008).Although innovation systems thinking is intuitively attractive, many find the concepts abstract and struggle to find practical and implementable ways of intervening to improve innovation capacity.Above all, IPs represent a possible solution (Homann-Kee Tui et al., 2013;Schut et al., 2015).Furthermore, IPs are a strategic instrument to improve collaboration for agricultural development in developing countries, even though they frequently focus on marginalized poor stakeholders (Swaans et al., 2014;Van Paassen et al., 2014;Schut et al., 2016).
Thus, IPs are more open than committees, which are frequently established with a set of members to address a specific issue.As a discussion forum that operates without the requirement for a defined legal framework, IPs also free institutions from institutional limitations (van Paassen et al., 2014).Accordingly, they are more flexible as conversational tools than consortia, which frequently call for a binding legal agreement between the parties involved.In this sense, IPs have been promoted by various stakeholders who are interested in resolving a particular challenge, be it a problem or an opportunity, as a social place for knowledge sharing and learning for innovation (Adekunle et al. 2013).
To do this, multi-stakeholder processes supported by IPs enable bottom-up searches for solutions to local bottlenecks (Pamuk et al., 2014).Multi-stakeholder procedures are used to identify obstacles, explore possibilities, look for solutions, and to spark collaboration and collective action (Sanyang et al., 2016).IPs have a greater potential to identify innovations appropriate for a particular context than mono-stakeholder groups of individuals with common backgrounds and experiences because they ideally engage multiple types of stakeholders with distinct backgrounds and experiences for a common interest (Horton et al., 2010).The priorities for innovation differ among IPs because each IP establishes its own priorities based on local preferences, possibilities, and limits.According to Horton et al. (2010), Pamuk et al. (2014), Sanyang et al. (2016), andSartas et al (2017), the success of IPs may be correlated with pre-existing social capital levels and the compatibility of innovations with local conditions.
An ideal and sustainable IP is determined by local preferences and draws stakeholders through an already-existing degree of social capital and a bottom-up participatory process (Sanyang et al., 2016).They may plan and carry out activities as a platform or manage the actions of specific members.IP can function either at the operational or strategic level.The strategic platform, which was established at a higher level to determine strategic orientations, alternatives, and axes, is in charge of the operational platforms, which are situated at the grassroots level.
The operational IP's responsibilities include joint diagnostics with various ground actors, validation of technological options through demonstrations, validation of mechanisms for accessing various services, such as credit, information, and market, support for farmers' organizations, and facilitation of interactions between farmers through cross-visits, exchange days, and field days, as well as facilitation of interactions between ground actors for experience sharing and learning, build actors' capacity (farmers, processors, traders, etc.).The strategic platforms' roles include enlisting facilitators (researchers, extensionists, and NGO professionals) to improve IP support, facilitating experience sharing between facilitators to enforce mutual learning, creating institutional support for IP, negotiating better market access for IP members, and facilitating mechanisms to access various services, such as credits, markets, and technical supports.The platform enables acts that none of the participants could have accomplished independently.A platform is likely to need facilitation and may go through a drawn-out initial phase of mutual learning and role-defining before it can get down to business because of its complicated membership and potential for conflict (Misaki et al., 2018).As a result, IP offers a favorable setting for interaction and the performance of connected stakeholders' tasks in the innovation process (Adekunle et al., 2010).
Several studies (Jiggins et al., 2016;Schut et al., 2016;Sparrow and Traoré, 2018) have investigated the impact and outcomes of IPs as well as their role in promoting agricultural development.Innovation platforms have been considered to address issues facing smallholder farmers and sustainably raise the standard of living for rural dwellers (ISPC, 2015;Maru et al., 2018).Additionally, previous studies on agricultural intervention using IPs have demonstrated the potential benefit of this participatory strategy in terms of influence on the outcomes for the livelihood of rural smallholder farmers in Africa (World bank 2012; Mapila et al. 2012;Schut et al 2015).
According to empirical studies, agricultural innovation systems have a positive impact on rural people's capacity to increase production by better utilizing their natural resource base (Gildemacher et al., 2009); improving food security and nutrition (Morris et al., 2007); diversifying their sources of income; and protect the environment (UN, 2008) in an African context.Furthermore, other studies have looked at the contribution of IPs to improved nutrition, food security, and poverty reduction (Wellard et al. 2013).The effect of innovation platforms (IPs) on food consumption and the reduction of rural poverty in central Africa was also assessed by Pamuk et al. (2015).Similarly, Ahimbisibwe et al (2020) analyzed the effects on household welfare of a platform for agricultural innovation in Uganda.Although numerous studies have demonstrated that IPs can encourage farmers´ innovation and enhance their standard of living (Kilelu et al., 2013;Lema et al., 2016), numerous studies have also highlighted flaws in the IP method.
For instance, IPs might unintentionally strengthen existing power dynamics (Cullen et al., 2014).Similar to this, IPs can legitimize the influence of vested interests, which may result in less-thanoptimal results (Hounkonnou et al., 2018;Schut et al., 2016).Others have claimed that IPs may not be adaptable enough to be guided by iterative learning procedures to handle developing challenges and have a limited ability to remove structural impediments (Kilelu et al., 2013;Klerkx et al., 2010).However, the effectiveness of IPs in achieving innovation outcomes depends on the context and is determined by the level of facilitation, the make-up of the stakeholders, and the power dynamics within IPs (Davies et al., 2018;Lamers et al., 2017;Cullen et al., 2014;Hounkonnou et al., 2012).Consequently, there is still much to learn about how a multistakeholder platform works and how it might affect how it develops (Lundy et al 2013;van Paassen et al 2013;Dror et al 2016).Rajalahti et al. (2008) suggest that because IPs are relatively new phenomenon in development, farmers' participation in IPs as well as its sustainability can be used as an indicator of how well they are working to improve welfare.
There is, however, little knowledge of what drives farmers to engage in IPs and the elements that support or discourage active engagement that is, sustainability.The mobilization of pertinent actors to address critical issues and maintain their participation is hampered by this knowledge gap.Understanding the interests of participants in IPs is crucial because it enables IPs to find the right actors and design policies that take into account the variety of interests present in IPs in order to maintain sustainability.Opening up spaces for involvement does not guarantee that those who engage will be eager to do so (Adeyemo and Kehinde, 2019;Kehinde and Kehinde, 2020).Farmers generally show little enthusiasm for or cooperation with many initiatives (Mulema and Muzar, 2015;Adeyemo and Kehinde, 2020).If farmers are reasonably satisfied that taking part will result in the desired benefits, they are more likely to participate in collective action.Even if participants invest time and effort in participating, they may become discouraged in participatory programs and believe that nothing ever changes.Additionally, participatory approaches fail to acknowledge how individuals varied, shifting, and multifaceted identities influence their decision of whether and how to engage in IPs (Cleaver, 2001;Matilda et al., 2020).
Finding the potential influences on farmers' participation in IPs is crucial.Most studies on IPs are based on impact evaluation.What currently lacks in the literature is how do we sustain farmers' participation in IPs?In actuality, the involvement of farmers in IPs has not yet been properly investigated.
There is little research on IP involvement that has used rigorous analytical techniques to overcome problems with causal effect estimation, like selection bias in IP participation (Ahimbisibwe et al., 2020).Many of them used weak quantitative techniques like probit regression modeling and cost-benefit analysis (Cadilhon, 2013;Martey et al., 2014;Iromini et al., 2021), hence they continue to be an unsolved mystery (Kilelu et al., 2013).To address the issues of selection biases, this study uses a double hurdle count model with a control function.Therefore, this paper offers the results of an empirical study whose objective is to evaluate IP participation and its sustainability.The study specifically outlines the socioeconomic characteristics of the farmers and identifies variables influencing farmers' participation in IPs and the sustainability of such IPs.In order to solve the sustainability concerns of IPs' participation, the criteria that have been recorded will serve as a basis.By describing the reasons that induce actors to join in IPs and the circumstances under which involvement is maintained, this paper contributes to the field of IPs, which is still comparatively understudied.For policymakers establishing IPs intervention programs and ensuring its successful participation, the paper also has practical significance.The study will also act as a guide for the implementation of IPs in other parts of Africa in the future.
The rest of the paper is structured as follows.The next section provides a description of the study's data and methodology.Section three summarizes and discusses the empirical findings, and Section four concludes by outlining the implications for policy.

Study area
The SSA CP tested the effectiveness of multistakeholder approaches in deriving impact in greater quanta in three Pilot Learning Sites (PLS) across sub-Saharan Africa.These sites are areas that have been benchmarked as representative locations for different regions in Africa.The areas are Kano-Katsina-Maradi which is the representative site for West Africa, The Lake Kivu area is the representative site for East and Central Africa, and the Zimbabwe-Mozambique-Malawi axis is the representative site for Southern Africa.
The data presented here comes from the Kano, Katsina, and Maradi (KKM) PLS, which spans a region with 18.3 million people across 83,900 square kilometers in Nigeria and the Niger Republic.The SSA CP used the Innovation Platform (IP) as the foundation for implementing the Integrated Agricultural Research for Development.The Innovation Platform is a physical and/or virtual forum that brings together all potential innovation actors, spanning the value chain and beyond, who are necessary for the generation of innovation for a commodity or system of focus, for constant interaction and joint learning on the development of innovation within the commodity chain, or value web of the system, for constant interaction and joint learning on the development of innovation within the communication network (Adekunle, 2005).The IP thus brings all these actors to play to enable them to easily deliver the three capitals -Human, Financial, and Social necessary for innovation and consequently the derivation of socioeconomic benefits.The PLT established a team under the direction of IITA to conduct a validation study for limits and potential entrance sites in all three KKM agroecological zones (CORAF, 2005).To examine the situation on four levels-community, area, state, and region-this committee was put together from a variety of institutions, including research, extension, nongovernmental organizations (NGOs), and the private sector.The majority (90%) of the time was to be spent at the local level of the community, and they worked in 20 villages chosen to be representative of the PLS utilizing participatory approaches.
Studies of a similar nature have been out in various PLS in East, Central, and Southern Africa.This report addresses the PLS component of the SSA CP now being implemented in Western Africa.The project is located on the Nigerian and Niger Republican Kano-Katsina-Maradi axis.Three Task Forces (TFs) make up the project, namely: (i) The Northern Guinea Savanna, which focuses on improving land productivity in the Northern Guinea Savanna zone through a multistakeholder approach integrating technical options, policy, and market access.
(ii) The Sudan Savanna TF, with the focus on 'sustainable agricultural intensification and integrated natural resource management in the Sudan Savanna in West Africa.' (iii) The Sahel Savanna Task Force, whose mission is to 'improve rural livelihoods of rural population through intensification, access to markets, and sustainable management of natural resources in the Sahel agro-ecological zone'.
The approved research entry points for each of the three TFs are: (i) Identification and promotion of suitable integrated pest management (IPM), indigenous knowledge systems (IKS), and crop production and storage technologies.
(ii) Promotion of suitable labor-saving technologies, such as traction and processing equipment.
(iii) Integrated management of soil fertility.
(iv) Crop-livestock production that is integrated.
(v) Promoting the right crop varieties, such as those that are early maturing, drought tolerant, and pest resistant.
(vi) Making use of the right technologies to develop irrigation potential (FARA, 2006).
The objectives of the TFs' projects are created and incorporated into a framework that is supposed to appropriately encapsulate the IAR4D's core idea.The implementation of the IAR4D is organized inside an IP system, as was mentioned in the introduction, and this system guided the choice of project sites based on the unique characteristics of the farming systems of each TF.

Sample selection
A total of 1800 households in the KKM PLS provided the data for this study.

Data analysis
This study examined the determinants of participation in IPs and its sustainability in West Africa using 1800 households.Firstly, the data were examined using descriptive statistics in order to better understand the socioeconomic characteristics of the farmers.The data were subsequently analyzed using the double hurdle count model to address the issues of unobserved heterogeneity and possible endogeneity.

Double hurdle count model
Determinants of farmers' participation in IP and sustainability in IPs were investigated using the Hurdle Poisson (HP) model.The decision to join an IP was used as a proxy for farmers' participation in IPs and the number of meetings attended by the farmers was used as a proxy for sustainability in participation in IPs.The model was selected because of the nature of the data.The dependent variable in the second hurdle is a zero-inflated and overdispersed count variable.HP model consists of a binary choice model such as the probit model to determine factors affecting farmers' participation in IPs and Zero-Truncated Poisson (ZTP) model to determine the factors affecting sustainability in participation in IPs.They are not, however, estimated at the same time.When modeling dichotomous or binary outcome variables, binary probit regression is commonly used.Studies previously conducted have shown that the standard Poisson regression was frequently used to determine the intensity of participation (Winkelmann, 2008;Cameron and Trivedi, 2013;Ehiakpor et al., 2020).
However, the fundamental constraint of the common Poisson regression model is that the conditional expectation and conditional variance must be equal, or equi-dispersion, (  ⁄ ) = (  ⁄ ) = .Unfortunately, this is not always the case.Overdispersion is the term used to describe the situation where the conditional variance exceeds the conditional expectation.While under-dispersion is the situation where the conditional variance is less than the conditional expectation.Additionally, the count data set contains instances of excess zeros.This means that the estimation of standard Poisson regression may be erroneous if we do not take the type of count data into consideration (Greene, 2002;Erdman et al., 2008;Ehiakpor et al., 2020).
However, when we look at the sample data that was used in this study, we see that every farmer showed up for at least two of the scheduled sessions, which means that the sample data is indicative of non-zero results in this study.It could result in a skewed estimation, if the zero-truncation data feature is not considered (Lord et al., 2005).Therefore, the bias brought on by non-zero outcomes can be corrected by using a zero-truncated Poisson model (Ehiakpor et al., 2020).According to Long and Freese ( 2014 The conditional probability equation has the following form: The conditional probability equation could be written as follows when we observed a certain count data, such as   = , and granted that k is a non-zero value: (  =    > 0,   ) = (  =&  >0   ) (  >0   ) (3) The zero-truncated Poisson model can also be stated in terms of empirical equations as: Where   is the independent variables,   signifies the error term, and β is the parameter that needs to be estimated.Based on the reviewed literature, the explanatory variables are chosen.Information were gathered by the researchers and processed using social science package software (SPSS) version 23 and using STATA software (ver.17, College Station, TX).

Socio-economics characteristics of the farmers
Table 1 provides a description of the socioeconomic characteristics of the farmers in the studied area.The monthly income of the farmer 13296 9773 Regarding IP participation, the age of the farmers is crucial.The average age of the respondents in the study area is 46 years.This demonstrates that a typical farmer in the study area is still in his or her economically and professionally active years.This finding corroborates the findings of Adeyemo and Kehinde (2020).The majority (88%) of the farmers are male.This indicates that men are more active and involved in IPs activities in West Africa.This finding corroborates the findings of Adeyemi et al. (2020).About 51% of the farmers are married.This reaffirms the fact that agricultural activities are primarily a family business run by the farm households in which their respective spouses assist in the farming operation thereby reducing labor costs.This could be the reason for their participation in IPs.The average years of education in the study is approximately 4 years.This implies that literate farmers are not involved in agricultural production in West Africa.This could hinder their participation in IPs.The farmers, in the study area, have an average of 29 years of farming experience.This study revealed that farmers in the study area, have many years of experience in farming and therefore could aid their participation in IPs.This finding corroborates the findings of Kehinde (2021).The average size of households in the study area is 8 persons.This suggests that the farmers in the study area have a sizeable household, which could act as a buffer against labor shortages on the farm.About 76% of the respondents are visited by extension agents in the last production season.The majority (68%) of the respondents are participants in agricultural IPs.IPs allow interactions among farmers in terms of information dissemination.The average number of meetings attended by farmers in IPs is 15.This could sustain their participation IPs.The average monthly income in study is ₦11531.The value of asset owned by the farmers is ₦143105.This could aid the farmers' participation in IPs.

Factors affecting farmers' participation in IPs and its sustainability
The double hurdle Poisson regression results are presented in Table 2.The independent variables were checked using the variance inflation factor for multicollinearity prior to the estimation of the double hurdle count model (VIF).The result, 4.97, shows that multicollinearity between the independent variables considered by the model is not an issue.
The results of the hurdle model are presented in two parts.The first hurdle shows the results of determinants of participation in IPs, while the second hurdle shows the result of factors that influence participation sustainability.In the first hurdle, the log-likelihood was significant (P=0.000),suggesting that the regressors have strong explanatory power.This shows that the entire model is well fit and significant at 1%.According to the results of the first hurdle, gender, age, household size, years of farming experience, number of female working-class members, number of young dependents, number of aged dependents, access to agricultural extension, Location_kano, and asset ownership have a significant impact on whether farmers participate in IPs or not.However, age and household size have a negative and significant impact on the decision to participate in IPs.This means that young farmers with small household sizes are more likely to participate in IPs.Gender, on the other hand, has a positive and significant effect on the decision to participate in IPs.This suggests that male farmers are more likely to participate in IPs.Years of farming experience has a positive and significant effect on the decision to participate in IPs.This suggests that experienced farmers are more likely to participate in IPs.The number of female working-class members has a positive and significant effect on the decision to participate in IPs.This suggests that farmers with a lot of female working-class members in the households are more likely to participate in IPs.The number of young dependents in the households has a positive and significant effect on the decision to participate in IPs.This suggests that farmers with a lot of young dependents are more likely to participate in IPs.The number of aged dependents has a positive and significant effect on the decision to participate in IPs.This suggests that households with a lot of aged dependents are more likely to participate in IPs.The access to agricultural extension services has a positive and significant effect on the decision to participate in IPs.This suggests that farmers with access to agricultural extension services are more likely to participate in IPs.The asset ownership has a positive and significant effect on the decision to participate in IPs.This suggests that farmers that owns asset are more likely to participate in IPs.The coefficient of district dummy for farmers located in kano has a positive and significant effect on the decision to participate in IPs.This suggests that farmers located in kano are more likely to participate in IPs.
An inverse Mills ratio was utilized as a covariate in the model to account for selection bias in the second hurdle.The fact that the IMR is not statistically significant indicates that selection bias was a concern.Therefore, it is justified to use a double hurdle model to estimate the factors that determine participation and its sustainability.Also, the likelihood ratio test of alpha substantially does not reject the null hypothesis that the errors do not display overdispersion in the second hurdle.As a result, the zero truncated Poisson regression model could not be dismissed in favor of the zero truncated NB regression.As a result, when compared to the zero truncated NB regression model, the zero truncated Poisson model provides an unbiased and consistent estimate.The findings of the second hurdle model reveal that gender, age, marital status, years of schooling, the number of female members of the working class, the number of young dependents, the number of aged dependents, access to extension services, location_kano and asset ownership play a significant role in determining the sustainability of participation in IPs.As a result, the age of farmers has a negative and significant impact on the sustainability of participation in IPs.The result suggests that an increase in the age of farmers decreases the sustainability of participation in IPs.Similarly, asset ownership has a negative and significant impact on the sustainability of participation in IPs.The result suggests that an increase in farmers' assets decreases the sustainability of participation in IPs.Gender, on the other hand, has a positive and significant impact on the sustainability of participation in IPs.The result suggests that being a male farmer increases the sustainability of participation in IPs.Marital status positively and significantly affects the sustainability of participation in IPs.This implies that being a married farmer increases the sustainability of participation in IPs.Years of education positively and significantly affect the sustainability of participation in IPs.The result suggests that an increase in years of farmers' education increases the sustainability of participation in IPs.The number of female workingclass members has a positive and significant impact on the sustainability of participation in IPs.The result suggests that increase in the number of female working-class members increases the sustainability of participation in IPs.
The sustainability of participation in IPs is positively and significantly impacted by the number of young dependents in the household.The findings imply that as the number of young dependents increases, so does IP participation sustainability.The number of aged dependents in the household significantly and positively affects the sustainability of participation in IPs.The result suggests that increase in the number of aged dependents increases the sustainability of participation in IPs.Similarly, access to extension service positively and significantly affect the sustainability of participation in IPs.This implies that farmers with access to extension services participate in IPs.Location_kano positively and significantly affect the sustainability of participation in IPs.This suggests that contact with farmers located in kano increases the sustainability of participation in IPs.
The farmers' decision to participate in IPs is influenced by their gender.Abebaw and Hailes (2013) and Balgah (2016) supports the findings, that families headed by males are more likely to participate in IPs than households headed by women since men often perform fewer reproductive tasks.However, the farmer's age has a negative effect on their decision to participate in IPs.This is in line with what Awotide et al. (2015) discovered in rural Nigeria.This is due to the perception that younger farmers are strong risk-takers than older farmers, making them more open to change such as participation in IPs rather than participation in cooperatives.Ito et al. (2012) also supported the idea that aged farmers typically choose to withdraw themselves from IPs, particularly when the expenses of membership outweigh the benefits.Furthermore, the size of the household has a negative impact on IP involvement.This finding contradicts with previous studies from Mojo et al. (2015) and Bernard and Spielman ( 2009), among others.Farmers' participation in IPs is positively impacted by the farmers' years of farming experience.This is probably because as the farmer gets older in farm work, he accumulates more human capital in the form of experience and increased household members which when re-invested into the farming business will increase yield.Consequently, he will then need a trustworthy route to secure the right income from the sale of his product, which will push him to join IPs.This result is consistent with that of Tesfamariam (2012), who discovered that the number of years spent working as a farmer and in a cooperative had a positive impact on participation in IPs.According to studies conducted in Ethiopia, involvement in an agricultural group is influenced positively by a farmer's experience (Bernard et al., 2008;Bernard and Spielman, 2009;Abebaw and Haile, 2013).In a household, the proportion of female workers, children, and elderly dependents has a positive impact on participation in IPs.This can be explained by the fact that consumption pressure from these set of household members may push farmers to join an IP.The results support studies by Bernard and Spielman (2009) and Mojo et al (2015).
All things being equal, farmers that have adequate access to extension services are more likely to join IPs, according to Ma et al. (2018), Abdul-Rahaman and Abdulai (2018) and Kehinde and Ogundeji (2022).This may be explained by the fact that the government and other partners' extension workers frequently persuade farmers to join IPs and embrace new production technology since some new inputs, such as seedlings and information on their use, are shared through IPs.This agrees with Nugussie (2010), who contends that exposure to training and visits is crucial to raising rural people's awareness of the benefit of participating in IPs.Farmer assets have a mixed impact on their involvement in IPs.For instance, the farmers' assets have a positive influence on the ability to participate in IPs.This is consistent with previous research by Asante et al. (2011), Bagher (2011), Francesconi and Heerink (2011), Abebaw and Haile (2013), and Mojo et al (2015).Contrarily, the level of participation in IPs is negatively influenced by the farmers' assets.This agrees with previous studies such as Karli et al. (2006) and Verhofstadt and Maertens (2014).Participation in IPs and marital status had a statistically significant positive relationship.An agricultural organization's membership was found to be significantly influenced by marital status in a study conducted in Poland (Banaszak, 2008), which may be explained by the fact that pressure from the household may push farmers to joining IPs.Years of education were found to have a positive impact on the decision to participate in IPs in this study, which may be explained by the fact that education enables farmers to comprehend the overall advantages of joining IPs.Findings from studies by Weber and Musshoff (2012), Verhofstadt and Maertens (2015), and Mojo, Fischer, and Degefa (2015) are in agreement with this finding.Conversely, education makes it easier to learn new information, such as market and input pricing, which seems to support the notion that farmers are more willing to join IPs (Ma et al., 2018;Mojo et al., 2017).The positive relationship between farmers located in kano and participation in Ips could be attributed to the fact that the farmers in kano understand the benefits of IPs.

Policy implication
This paper adds evidence for a better understanding of the determinants of participation in IPs and its sustainability.

CONCLUSIONS
This study investigated the factors that affect IP participation and its sustainability.A multistage sampling technique was used to obtain the data for the study.The data were analyzed using the Double hurdle count model.According to the results of the first hurdle, gender, age, household size, years of farming experience, number of female working-class members, number of young dependents, number of aged dependents, access to agricultural extension, Location_kano, and asset ownership have a significant impact on whether farmers participate in IPs or not.While the findings of the second hurdle model reveal that gender, age, marital status, years of schooling, the number of female members of the working class, the number of young dependents, the number of aged dependents, access to extension services, location_kano and asset ownership play a significant role in determining the sustainability of participation in IPs.This study concluded that raising farmers' income, education, and frequency of extension contacts will result in more active and sustainable participation in IPs.Therefore, we recommend governments to set up efficient programs and institutional frameworks that enhance farmer knowledge, the frequency of extension contacts, and income to enhance farmer participation in IPs.Second, in order to maximize participation and maintain the platform's sustainability, IP facilitators need to educate and target younger farmers.Finally, the IPs must be targeted at poor farmers as asset ownership discourages the likelihood to sustain participation in IPs.Farmers can escape extreme poverty and enhance their revenue by increasing their participation in IPs.Future studies, though, must investigate the characteristics of people who stop participating in IPs.
), the standard Poisson regression model serves as the foundation for the zero-truncated Poisson model, which is a variant of that model: (  /  )  ) = (−  ) while the likelihood of observing non-zero outcomes is denoted as (  = 0/  ) = 1 − (−  ) The survey was conducted by TFs as a part of the SSA CP, which was coordinated by FARA and sponsored by the governments of Italy and Norway as well as the European Union (EU) and the UK Department for International Development (DfID).