Influence of Microfinance Institution Technical Systems on the Performance of Dairy Farmers in Kakamega County, Kenya

Objective of the study sought to investigate the influence of MFI technical systems on the performance of dairy farmers in Kakamega County. The aim of this objective was to find out how MFI technical systems have impacted on dairy farming performance in Kakamega County. Therefore, it was hypothesized that there is no significant relationship between the MFI technical systems and performance of dairy farmers in Kakamega County. MFI technical systems were measured by training, workshops, seminars which offer technical systems on marketing, breeding technologies, marketing, livestock health technologies and milk production technologies.This study adopted exploratory research design. The target population in this study consist of small scale dairy farmers deriving their capital from 17 micro-finance institutions.Descriptive and inferential statistics was used for the purpose of data analysis. To test the research hypotheses, Pearson correlation and regression was used to measure the general relationship between dependent and independent variables at significance level of 0.05. The findings were presented using tables, models and charts. The findings revealed that Micro Finance Institutions systems have significant contribution to the performance of dairy farmers in Kakamega. Microfinance technical systems played significant role in the dairy farming performance as it enhances quality and quantity of dairy products through upscale of production technologies.


Introduction
Nabiswa and Samba (2016) indicated the most challenges facing small scale dairy farmers in Kenya is lack of appropriate technical skill from feeding, disease control, marketing and production. This has reduced their returns and affected sustainability of dairy projects. According to Makokha (2015) most dairy farmers in Kakamega County lack technical skill in dairy production and this has affected milk yield in the County. Abdul-Moomin (2012) while investigating the role of microcredit on smallholder livestock production in Wa Municipality, Ghana, he recommended that microfinance should offer technical systems on breeding, feeding and other veterinary services. Mutai and Osborn (2014) as well as Madlani (2013) asserted that microfinance and other institutions should provide more training programmes so as to uplift the dairy sector which has suffered due to proper awareness. This was results to increase in production and improve the productivity of milk.

2.0LITERATURE REVIEW
In Zimbabwe,  argues that the SME sector attracts a low priority to financial training and are often unwilling to participate in programs that require them to finance the costs these enterprises eventually are weak in cash management, marketing strategies and finance. The study further concluded that business supported by MFIs should be trained to ensure their growth. OECD (2013) found out that in New Zealand, United Kingdom, Belgium, Poland, Turkey and Canada, the following reasons why owners of agribusinesses do not participate in training programs: lack of time, very expensive and difficulty in accessing its relevance to the needs of the enterprise. Furthermore a conclusion was drawn that firms that did not participate in these training programs did so because they believed they already have or can recruit the skills the enterprise required. Therefore microfinance institutions need to create ways of measuring the impact of financial skills training to the SMEs.
However, Wright (2000) is not enthusiastic about the role of microfinance institution training to businesses supported and thinks that these funds should be diverted to other projects desperately needed such as health of the people in an organization and there is inadequate learning from the training programs offered by Microfinance Institutions funds could be used in other projects that might help the businesses more. Mbithi (2016) attested that even though MFI training is effective on the performance of businesses supported, most of MFIs do not explicitly budget for their clients. This means that most of the MFIs are limited to provide technical systems and training as financial obligation is prioritize first.
Microfinance Institutions need to train and provide technical systems to owners to have skills for specific production, business management and access to markets in order to make profits from the financial resources they receive. Financial skills training can improve the ability of the low-income earners to operate enterprises either directly or indirectly. Complexity of financial decisions requires that business owners are able to make informed choices on saving, borrowing, spending and investing their money (OECD, 2013). It is therefore important to investigate the influence of MFI technical and training systems on the performance of dairy farming businesses as it is often these vulnerable businesses affected by lack of financial capability. The effects of a lack of financial capability as highlighted by McQuid and Egdell (2011) are not only financial but may lead to wider problems for the individual, household and beyond, including debt, higher stress and reduced wellbeing.
Kenduiwa, Mwonya and Kinuthia (2016) used a cross sectional survey research design to investigate influence of smallholder dairy farmers' participation in microfinance on dairy farming in Longisa sub-county, Bomet County, Kenya. Simple random sampling was used to select 152 respondents for study. The questionnaire used to collect data was developed by the researcher and validated by experts in applied community development. The study revealed that training has a significant and positive influence on productivity of dairy farmers in the study area. However, the study did not indicate which specific productivity indicators were measured in the study to arrive at the conclusion.
Kimutai and Kipchumba (2016) sought to determine the effect of provision of loans and training systems on farmers' livelihood by financial association in the North Rift Region, Kenya. The study employed descriptive survey and explanatory research design. The target population of this study comprised 12,745 members/farmers drawn from 8 financial service associations operating within North Rift Region. The study used stratified sampling technique to select the 387 members as the study sample size. The findings of the study revealed that training systems impact positively on the livelihood of dairy farmers. The study did not target microfinance institution but also targeted other financial institution such as SACCOs which have the capacity to upscale dairy productivity. The study also did not measure performance of dairy farmers but it was based on household livelihood. Mutua (2017) carried out a study to show the effect of Microfinance systems on poverty reduction in Makueni County. Descriptive research design was adopted. The study targeted 6 deposit taking institutions in Makueni County. Systematic simple random sampling method was used to select 244 MFI members from the deposit taking microfinance. Primary data was collected using questionnaires and presented by descriptive statistics methods like pie charts and graphs. The study found that Microfinance training has positive and significant effect on Poverty reduction in Makueni County. One of the key shortcomings of the study was that it sought to investigate effect of MFI systems on poverty reduction rather than on performance of dairy farmers. Therefore, the study did not also establish the effect of technical systems offered by MFI. complete census of 210 MSEs. Primary data was collected using structure questionnaires. The study findings revealed MFI training systems have not contributed positively. The study recommends there is need for MFIs and MSEs stakeholders to come up with measures of addressing the poor performing training component of MFIs systems in Kenya.  reviewed the effects of microfinance systems on the performance of MSMEs using an explanatory research design. The study targeted 429 MSMEs registered by the Kiambu Municipal Council and sampled 270 enterprises. The study utilized multiple regression analysis set draw inferences on the study using SPSS statistical package. The study found managerial training to be statistically significant in determining the performance of MSMEs. This study concludes that increasing provision levels of micro finance will result in increased performance of micro enterprise. The study limited itself to managerial training of Micro, small and medium enterprises leaving out general training and technical systems that may have impact on performance. The study also did not sample any of dairy farming respondents.

Knowledge-based Theory
Knowledge-based theory was postulated by Penrose (1959). This theory underlines the role of knowledge in increasing productivity within different enterprises and the economy. Therefore, enterprises and economies which have highly educated and financially trained manpower are likely to be high performers compared to those that lack these key resources. In the finance context, the reason is that well-educated and financially trained dairy farmers are proactive and quick at learning and applying new skills to improve efficiency, productivity, risk taking and innovativeness of their enterprises (Brue,et al. 2009).
The knowledge-based theory distinguishes between two types of learning on the basis of the mode or context within which it occurs. First, there is exploitative learning, which is external to the dairy farmers and therefore must be acquired and its acquisition is dependent on the duration, content, facilitator and follow-up programme. Second, it is important to acquire explorative learning, which obtains from inside the enterprises and thus can occur only through internal experiments and hence is experiential in nature. Therefore, this study relates to acquisition of knowledge by dairy farmers and their technical systems. Technical systems and learning by doing are fundamental processes of knowledge development for dairy farmers. Technical systems and training can be used to integrate the learning and attitudes with behavior. From a strategic point of view, well informed dairy farmers could easily make correct decisions concerning the growth of their enterprises. Dairy farmers with such trainings manifest various knowledge bases on breeding, milk production, marketing, feeding, disease control and financial management. These decisions invariably distinguish between high performing and low performing enterprises (Diamond, 2005) Technical systems and training, therefore, impact upon breeding, milk production, marketing and enterprise development of the dairy farmers. Moreover, it influences the vision, mission, culture, and values of the enterprises. Therefore, the rationale of using this theory was that it underscores the vitality of the level of technical systems and training of the dairy farmers which positively impact on their performance.

RESEARCH METHODOLOGY AND DESIGN
This study adopted exploratory research design. The design was chosen because it design establishes causal relationships between variables. The emphasis is on studying a situation or a problem in order to explain the relationship between variables (Saunders, Lewis & Thornhill, 2007).Creswell (2005) asserts that explanatory research design can be used to predict an outcome such as performance of dairy farmers. In this case it involved gathering of data to determine the influence of microfinance institution systems on the dairy farming performance.The study carried out in Kakamega County which is one of the forty seven counties in Kenya. The county has 12 sub counties. The study particularly dealt with dairy farmers who get their funding from Microfinance institutions which has a population of 1310.Samples were chosen using stratified sampling to ensure each sub county in the county was proportionally represented. For primary data Krejcie and Morgan formula was used S = X 2 NP (1-P) d 2 (N-1) + X 2 P (1-P) Where; S is the desired sample size X 2 is the table value of chi-square for one degree of freedom at desired confidence level which is 1.96 X1.96= 3.841  Vol.10, No.18, 2019 164 N is the population size (1310) P is the population proportion assumed to be 0.5 since this will provide maximum sample size and d is the degree of accuracy expressed as a proportion 0.05 S = 3.8416 X 1310 X 0.5 (1-0.5) =297.2250703which is 297 Respondents. 0.05 2 (1310-1) + 3.8416 X 0.5(1-0.5) The distribution per Sub County was done proportionally as shown in Table 1

Response Rate
Two hundred and ninety seven questionnaires were issued to dairy farmers of sampled MFI. Two hundred and fifty eight were returned. The response rate was 88.9%. The questionnaire yielded over 60% of response rate which is satisfactorily according to Mugenda and Mugenda (2008).

Microfinance institutions Technical systems
The sampled respondents were asked to indicate the extent of their agreement on five statements related to Microfinance institutions technical systems. The response range from 1 strongly disagree, 2-disagree, Uundecided, 4-agree and 5-strongly agree. The metric means and standard deviation of the six statements were also computed.   (152)of the sampled respondents agreed and strongly agreed respectively that MFIs assist farmers in obtaining inputs for the dairy farming. A mean of 4.35 indicated that MFIs assist farmers in obtaining inputs for the dairy farming. The results further revealed that 60.9 %( 157) of the sampled respondents strongly agreed that technical systems obtained from MFI have enabled me to change dairy production system while 24%(62) agreed that on the same. A mean of 4.34 implied that technical systems obtained from MFI have enabled me to change dairy production system.
The results further revealed that 23.6 %( 61) and 58.5 %( 151) of the sampled agreed and strongly agreed that their MFIs assist farmers in the mode of marketing of the dairy products. A mean of 4.31 implies that MFIs assist farmers in the mode of marketing of the dairy products. Further, 48.1 %( 124) of the sampled respondents strongly agreed that MFIs organize trainings for its members on various technologies to improve productivity and 23.3 %( 60) agreed on the same. A mean of 3.98 implied that most of MFIs organize trainings for its members on various technologies to improve productivity.

A simple linear regression between Technical systems and Dairy farmer performance
A simple linear regression was carried to assess the influence of Technical systems on the dairy farmer performance in Kakamega County. This entails composite variable of Technical systems index which was mean obtained from six metrics that was used to measure Technical systems in this study. Similarly, the composite value of dairy farmer performance was obtained by getting mean of five metrics that was used to measure dairy farmer performance. The results are presented in Table 19.  The proportion of variance in Dairy farmer performance explained by the independent variable (Technical systems) is 39.3% (R 2 =0.393). From the findings, the F ratio is greater than 1, as indicated by a value of 166.057, which means that improvement due to fitting the model is much greater than the model inaccuracies (F(1,257)= 166.057, P=0.000).. This implies that technical service is useful predictor of dairy farmer performance. From the findings presented in Table above, technical systems carried positive significant predictive power (B=0.446, p=.000) implying that a unit change in technical systems would result to significant change in dairy farmer performance value by 0.446 units. Therefore, the linear regression results indicated that there was a statistically significant positive relationship between technical systems and dairy farmer performance. The study developed analytical model for predicting dairy farmer performance from technical systems is stated in the form of: Dairy farmer performance=1.843+0.446Technical systems

Summary of findings
The objective of the study was to investigate the effect of MFI technical systems on the performance of dairy farmers in Kakamega County. Descriptive results indicated that MFI were offering to great extent technical systems to their clients. This included information on dairy farming input especially feed and fodder, breeding technologies such as Artificial Insemination (A.I). The study also found that MFI organized capacity building with dairy farmers through training as well as collaboration with other institution and organization dealing with dairy value chain. This has seen farmers accessing market; add value to their milk such as yoghurt and other by-products. The income of farmers has increased due to increase in milk quantity and accessibility of market. The results indicated that Technical systems had a statistically significant influence on the dairy farming performance in Kakamega. The hypothesis that there is no significant relationship between the Technical systems on the dairy farming performance was rejected by the study.

Conclusion
Objective of the study sought to test the first null hypothesis which posits: There is no significant relationship between the MFI technical systems and performance of dairy farmers in Kakamega County. From the findings, there was sufficient evidence to reject the null hypothesis and conclude that there is significant relationship between the MFI technical systems and performance of dairy farmers in Kakamega County. Increase in technical systems from Micro Finance Institutions such as feed and fodder, breed technologies, marketing, milking technologies and up scaling of the technologies. Most small scale farmers are unable to access technical systems especially extension systems and capacity building as this has impact dairy farming performance. Both governments (National and County) have been unable to offer this service effectively and therefore, alternative actors such as Micro Finance Institutions have significant role to play to ensure farmers who are their client receive the required technical systems so as to service the credit and the same time to meet their livelihood needs. Most of the Micro Finance Institutions extending Micro Finance Institutions systems to dairy farmers in Kakamega County have aggressively offered technical systems that are beneficial to dairy farmers.

Recommendations
From the conclusion, the following recommendations were made in respective to the objective of the study Offering financial systems alone is not assurance that dairy farming performance would increase. It was found that some of the Micro Finance Institutions were offering technical systems while others were not. The study recommended that Micro Finance Institutions need to improve on the technical systems offered to dairy as not all Micro Finance Institutions were found to offer each aspect of technical systems that is needed to improve dairy farming performance. Specifically, the Micro Finance Institutions need to collaborate with other organization and institution both government and non-government to ensure that farmers receive technical systems which will support dairy farmers. Through this collaboration, Micro Finance Institutions would ensure that their systems are been utilized in a profitable way therefore achieve social and economic mission of Micro Finance Institutions. The government should make it compulsory for Micro Finance Institutions offer credit to dairy farmers to support technical systems through corporate social responsible programs. On the other hands, dairy farmers should be support technical systems offered by Micro Finance Institutions especially capacity building through self-help groups formations.