Microcredit Rending on Maize Production - 4675 Words

Microcredit Rending on Maize Production (Essay Sample) Content: IMPACT OF GROUP MICROCREDIT LENDING ON MAIZE PRODUCTIVITY IN KENYANameProfessorInstitutionCourse nameDateABSTRACTMicrofinance in Kenya is considered as one of the most important and effective mechanisms in the implementation of the government program to reduce poverty and to increase economic growth. Almost 10 years have passed since micro-credit programs have been introduced in Kenya. However, little is known about the effects of microfinance on smallholder farmers' household food security in Kiharu constituency in Murangà ¢Ã¢â€š ¬a. This study sought to examine the contribution of microfinance services to the food security of smallholder farmers in Murangà ¢Ã¢â€š ¬a county. Descriptive Analysis, Logit Model and Generalized Propensity Score will be used to analyze the data. Primary data will be collected from 200 respondents randomly selected from credit beneficially and non beneficially groups in Kiharu constituency using structured questionnaires. The Logit Model will be used to analyze factors that influence the smallholder farmers' participation in microfinance services. Results from Generalized Propensity Score (GPS) (for binary treatments) will be used to assess whether households who had participated in microfinance services had increased their total annual maize yield compared to non-participants. The study will recommends policy that will increase smallholder farmers participation in microfinance services and hence agricultural productivity. Furthermore it will recommend whether group credit lending to be promoted by the Government in order to provide an instrument for mobilizing savings and extending credit.-CHAPTER ONE1.0 INTRODUCTION1.1 Background of the StudyMaize is an important staple crop in Kenya, where it provides food security and employment . About a third of Kenyan population is food insecure. This is largely dependent on the availability and affordability of maize (GoK, 2010). Maize is predominantly grown in Trans Nz oia, Nakuru, Bungoma Uasin Gishu and Murangà ¢Ã¢â€š ¬a counties (GoK 2010; Njenga, 2013).Maize production in Kenya is characterized by high small holder participation at 75% of the overall production (CBS, 1999; Olwande, 2012). The national demand of maize in Kenya is more or less 43 million bags per year while the supply is only 34 million bags (GoK, 2013).The Government of Kenya has set up micro finance institutions to reach the poor, who were neglected by banks due to high interest rates and need of collaterals, in a sustainable manner (Kimuyu and Omiti, 2000). The access to credit allows these poor farmers adopt technologies, improve on productivity, food security and enhance on profitability (Issam, 2010).Murangà ¢Ã¢â€š ¬a County agricultural land is limited, access to farm inputs is irregular, rain is inadequate and unreliable, the soil fertility is poor, the farming methods are unsatisfactory and access to credit is patchy (Olwande, 2012).The average maize yields in Murang à ¢Ã¢â€š ¬a County are as low as about 10 bags per acre against a potential of 20 to 35 bags per acre . According to FAO 2012 interventions such as improving agricultural and post- harvesting technologies, expanding the quantity and quality of available farmland and increasing access to agricultural inputs may improve food availability and can address to chronic food insecurity. Investing in agricultural enterprises through microcredit services is a potential option for improving the income and food security of rural households in Kenya . Murangà ¢Ã¢â€š ¬a County is served by a number of Microfinance Institutions. The substitute microfinance institutions include farmersà ¢Ã¢â€š ¬ cooperative unions such as Mugama Farmers Sacco, Murata Sacco and Unaitas, among others. However the agricultural productivity remains low despite the presence of the microfinance institutions. According to IFAD (2009) access to microcredit by farmers can help to substantially reduce vulnerability and en hance food security. Access to credit enables farmers to purchase pesticides and other chemicals to control pests and diseases that attack the crop or even the harvested grains leading to destruction hence loss to the farmers.This study therefore seeks to establish the linkage between the microfinance institutions and the low agriculture productivity in Kenya and suggest on policy to make MFI more business-driven and commercialization without exploitation of the poor.1.2 Statement of the ProblemMaize is an important staple food in Kenya and provides food security and income in many households. There is a chronic deficit in the supply of maize in Kenya, which can be filled through increasing farm productivity. Murangà ¢Ã¢â€š ¬a County is among the leading producers of maize however productivity is low causing household food insecurity and poverty. There is a network of microfinance institutions in Murangà ¢Ã¢â€š ¬a which provide credit to smallholder farmers. However farmers are fa ced by lack of access of credit through commercial bank and has resulted to group microcredit lending strategy . The group lending credit model is popular among the small holder farmers in Murangà ¢Ã¢â€š ¬a. Despite MFI operating for sometimes in Murangà ¢Ã¢â€š ¬a little is known on their contribution to household food security and productivity . This study will be conducted to assess how MFI are contributing to household food security .1.3 Objective of the studyThe main objective of this study is to examine the impact of group microcredit lending on maize productivity by small scale farmers in Kiharu Constituency Murangà ¢Ã¢â€š ¬a County, Kenya. This will be achieved through the following specific objectives. To assess the profile of group microcredit lending sources in Kiharu Constituency Murangà ¢Ã¢â€š ¬a County, Kenya. To asses the participation levels of smallholder maize farmers in group microcredit by smallholder maize farmers in Kiharu Constituency Murangà ¢Ã¢â€š ¬a County, Kenya. To assess the impact of group microcredit lending on maize productivity in Kiharu Constituency Murangà ¢Ã¢â€š ¬a County, Kenya.1.4 Hypothesis. 1 There is no significant participation through group microcredit by smallholder maize farmers in Kiharu Constituency in Murangà ¢Ã¢â€š ¬a County, Kenya. 2 There is no significant impact of group microcredit financing on maize productivity in Kiharu Constituency in Murangà ¢Ã¢â€š ¬a County, Kenya.1.5 Significance of the StudyThis study would be beneficial to the banking sector, MFIs, policy makers, stakeholders, service providers and implementers of the ongoing reforms in the maize sector. The findings of this study may help the County Government in formulating its policies to improve maize farming. The study will provide evidence that MFI have a positive effect on food production and that financial support is justified. The studyà ¢Ã¢â€š ¬s findings may also be beneficial to scholars and researchers by contributing to the existing stock of knowledge and or stimulating further research on MFIs and maize production.1.8.1 Conceptual and Theoretical FrameworkFigure 1.1 Shows the conceptual framework of the participation in micro credit and impact of group lending micro financing on maize productivity by small-scale farmers. The marginal contribution of credit is to bring input levels to optimal levels, thereby increasing output and, because the quantity of land is fixed, yield increases. The dependent variable is the yield of maize, which is measure in kilograms per hectare. The independent variables are the profile of the Microfinance and credit access. The associations of the two variables give rise to other intervening variables such as market accessibility, quality of yield, access to farm inputs and increased units of production. The group credit model of agricultural Credit is based on that group collateral assumption can substitute physical collateral.Intervening Variables Profile of Microfi nanceGroup based collateralGroup solidarity, leadership ,social groupDependent variable: ImpactChange inYield Independent Variables Farmer characteristics(Age of Household head,, sex of household head, household size ,extension services, land size, education of household head, House quality, Non farm income, Locality) Dependent variable: MFI Participation decision Y =1 Y=0 Dependent variable: ImpactYieldFigure 1.1: Conceptual framework showing linkages between independent and dependent Variables in the study (Adopted from Feder et al., 1985).The study is based on profit maximization in producer theory as illustrated in Household maximizes profit model (à Ã… ¸) subject to constraints from fixed factors of production such as land (L), Credit (K) and others specified as (Z).Group credit lending efforts mainly targets variable K. Max à Ã… ¸= PyY - Px Xs.t Y=f (L, K, Z).where Y is production, L= Land, K =Capital, and Z .The lack of credit deviates from th e optimum and leads to demand for credit as an input for profit maximization. Therefore credit can be viewed as an input in the classical production function and the household as the basic decision making unit. The household characteristics consequently impact on the decisions making process in joining a microfinance institution.CHAPTER TWO2.0 LITERATURE REVIEW2.1 IntroductionThis chapter provides a review of literature related to the impact assessment using Generalize propensity score (GPS) and those related to participation of farmers in microcredit institutions. This section also identifies the knowledge gap that this study will attempt to fill.2.2 The Impact assessment using Generalized propensity score (GPS) and other methods.Girabi and Mwakaje (2013) include in their study the significance of microcredit benefits to agricultural productivity in r...