| Peer-Reviewed

Influence of Enterprise Decision Making on Agribusiness Loans Default Rate in Agricultural Finance Corporation, Mount Kenya Region

Received: 12 May 2023    Accepted: 29 May 2023    Published: 9 June 2023
Views:       Downloads:
Abstract

Farming projects funded using Agricultural Finance Corporation (AFC) capital are successful due to input of effective and efficient decisions. Farmer decisions have been observed to affect the loan default rate. The default rate for these loans has been reported to be 20.33%, which by standards is high since the yardstick for all types of loans in Kenya is 10%. This study aimed at establishing the influence of enterprise decision making on AFC loan default rate in Mount Kenya Region. Descriptive research design was used to study a population of 3,002 agribusiness borrowers in the 11-branch network region. Using systematic random sampling with an interval of 10, a sample of 300 respondents was obtained. Primary data on enterprise decision making was collected using a structured questionnaire. Statistical Packages for Social Sciences (SPSS V.27) and Stata version 15 was used to analyse data. To establish the effect of variables in estimating default rate, regression analysis was utilized. F-statistic was derived by performing ANOVA. The econometric model that was used to specify the statistical relationship between the independent variable and AFC loan default was binary logistic regression which showed that the all the four indicators of enterprise decision making that were used in the model explained 36.98% of AFC loan default rate. Results of the study revealed that agricultural enterprise diversification was significant at 5% while implementation of purposed project, land size and land use dynamics were significant at 10%, 5% and 1% levels of significance. Agricultural enterprise diversification and implementation of purposed project were found to have 7.6% and 6% associations with default respectively. In mitigation of default, borrowers should make decisions of using good agricultural practices of enterprise diversification and avoid diverting their loans to non-agribusiness projects. They should also make decisions on reasonable landholding which should be engaged in production while paying attention to dynamics of land use in regard to parcel purposes and consolidation. Farmers may utilize the output of this study to make effective and proficient decisions about good agricultural practices that are motivated by integration of credit into farming. The study recommends resource use-efficiency by encouraging borrowers to adopt land use and credit use strategies, use effective farming technologies, adopt risk mitigation through insurance schemes and form common interest groups to tap the dynamic externalities of grouping.

Published in International Journal of Economic Behavior and Organization (Volume 11, Issue 2)
DOI 10.11648/j.ijebo.20231102.16
Page(s) 96-106
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

AFC Loan, Default Rate, Enterprise, Enterprise Decision Making, Repayment

References
[1] Cong, S. (2022). The Impact of Agricultural Land Rights Policy on the Pure Technical Efficiency of Farmers’ Agricultural Production: Evidence from the Largest Wheat Planting Environment in China. Journal of Environmental and Public Health, 2022.
[2] Msomi, T. & Olarewaju, O. (2022). Nexus of Loan Re-payment Plans, Interest on Loans and the Sustainability of Small and Medium Enterprises in South Africa. African Journal of Inter/Multidisciplinary Studies, 4 (1), 205-216.
[3] Darby, J., Fugate, B. & Murray, J. (2022). The role of small and medium enterprise and family business distinctions in decision-making: Insights from the farm echelon. Decision Sciences, 53 (3), 578-597.
[4] Muhongayire, W. (2012). An economic assessment of the factors influencing smallholder farmers' access to formal credit: a case study of Rwamagana district, Rwanda (No. 634-2016-41502).
[5] Ameh, M., & Lee, S. (2022). Determinants of Loan Acquisition and Utilization among Smallholder Rice Producers in Lagos State, Nigeria. Sustainability, 14 (7), 3900.
[6] Aberi, A. & Jagongo, A. (2018). Loan default and performance of youth enterprise development fund in Dagoretti South Constituency, Nairobi County, Kenya. International Academic Journal of Economics and Finance, 3 (2), 1-20.
[7] Liu, X., Wang, X., & Yu, W. (2023). Opportunity or Challenge? Research on the Influence of Digital Finance on Digital Transformation of Agribusiness. Sustainability, 15 (2), 1072.
[8] Ullah, A., Arshad, M., Kächele, H., Zeb, A., Mahmood, N., & Müller, K. (2020). Socio-economic analysis of farmers facing asymmetric information in inputs markets: Evidence from the rainfed zone of Pakistan. Technology in Society, 63, 101405.
[9] Fidelity Investments. (2021). Why Diversification Matters. Fidelity Investments. Retrieved from: https://www.fidelity.com/learning-center/investment-products/mutual-funds/diversification.
[10] Mosnier, C., Benoit, M., Minviel, J. & Veysset, P. (2022). Does mixing livestock farming enterprises improve farm and product sustainability? International Journal of Agricultural Sustainability, 1-15.
[11] Kray, A., Heumesser, C., Mikulcak, F., Giertz, Å. & Bucik, M. (2018). Productive Diversification in African Agriculture and its Effects on Resilience and Nutrition. Disclosure.
[12] Ramanujam, V. & Vidya, K. (2017). A Study on the credit repayment behaviour of borrowers. Int Res J Business and Manage, 10 (8), 9-18.
[13] Adusei, C. (2017). Determinants of Agribusiness Entities Loan Default in the Tamale Metropolis of Ghana. European Journal of Accounting, Auditing and Finance Research Vol. 5 No. 3, pp. 1- 20, March 2017.
[14] Gichuki, C. & Kamau, C. (2022). Financing Agribusiness: Potential Determinants of Financial Inclusion for Smallholder Rural Farming Communities in Kenya. International Journal of Rural Management, 18 (3), 376-393.
[15] Kislingerová, S. & Špička, J. (2022). Factors influencing the take-up of agricultural insurance and the entry into the mutual fund: A case study of the Czech Republic. Journal of Risk and Financial Management, 15 (8), 366.
[16] Njeru, C. (2016). Effect of Micro Factors on Financial Sustainability of Informal Finance Groups in Mwea Constituency (Doctoral dissertation, KCA University).
[17] Zhong, S., Li, X. & Ma, J. (2022). Impacts of land finance on green land use efficiency in the Yangtze River Economic Belt: A spatial econometrics analysis. Environmental Science and Pollution Research, 1-19.
[18] Henning, I., Bougard, A., Jordaan, H. & Matthews, N. (2019). Factors affecting successful agricultural loan applications: the case of a South African credit provider. Agriculture, 9 (11), 243.
[19] Rasel, S., Heijman, W. & Reinhard, S. (2022). Economic geography and entrepreneurial diversification in the agricultural sector. Regional Studies, Regional Science, 9 (1), 347-370.
[20] Yeboah, E., & Oduro, I. M. (2018). Determinants of loan defaults in some selected credit unions in Kumasi Metropolis of Ghana. Open Journal of Business and Management, 6 (3), 778-795.
[21] Nwafor, O., Agu, F., Anigbogu, T. & Umebali, E. (2018). Loan Repayment Behaviour among the Member of Farmers’ Multipurpose Cooperatives Societies in Anambra State. International Journal of Community and Cooperative Studies, 6, 28-49.
[22] Hainz, C. & Danzer, A. (2015). Property rights, collateral and interest rates. Evidence from Vietnam.
[23] Ramashia, N. (2019). Determinants of agricultural loan repayments: the case of MAFISA funded farmers in uMkanyakude, KwaZulu-Natal province, South Africa (Master's thesis, Faculty of Commerce).
[24] Nassoro, G. & Jaraj, K. (2022). Challenges small and medium enterprises (SMEs) face in acquiring loans from commercial banks in Tanzania. African Journal of Business Management, 16 (4), 74-81.
[25] Nasereldin, Y., Chandio, A., Osewe, M., Abdullah, M. & Ji, Y. (2023). The Credit Accessibility and Adoption of New Agricultural Inputs Nexus: Assessing the Role of Financial Institutions in Sudan. Sustainability, 15 (2), 1297.
[26] Dubale, S., & Beshir, H. (2020). Factors Affecting Loan Repayment Performance of Smallholder Farmers in Ethiopia. Agriculture, Forestry and Fisheries, 9 (3), 75.
[27] Baklouti, I. (2013). Determinants of microcredit repayment: The case of Tunisian Microfinance Bank. African Development Review, 25 (3), 370-382.
[28] Fentahun, G., Amsalu, T. & Birhanie, Z. (2023). Farmers’ perceptions about the influence of land fragmentation and land quality on sustainable land management in the upper Lake Tana Basin: Evidence from Dera District. Cogent Economics & Finance, 11 (1), 2160132.
[29] Jiang, Y., Long, H., Ives, D., Deng, W., Chen, K. & Zhang, Y. (2022). Modes and practices of rural vitalisation promoted by land consolidation in a rapidly urbanising China: A perspective of multifunctionality. Habitat International, 121, 102514.
[30] Lemaire, G., Franzluebbers, A., de Faccio Carvalho, C. & Dedieu, B. (2014). Integrated crop–livestock systems: Strategies to achieve synergy between agricultural production and environmental quality. Agriculture, Ecosystems & Environment, 190, 4-8.
[31] Iftikhar, S. & Mahmood, H. (2017). Ranking and relationship of agricultural credit with food security: A district level analysis. Cogent Food & Agriculture, 3 (1), 1333242.
[32] Weigel, R., Koellner, T., Poppenborg, P. & Bogner, C. (2018). Crop diversity and stability of revenue on farms in Central Europe: An analysis of big data from a comprehensive agricultural census in Bavaria. PLoS One, 13 (11), e0207454.
[33] Daniel, W. & Cross, C. (2018). Biostatistics: a foundation for analysis in the health sciences. Wiley.
[34] Cronbach, M. & Hedge, R. (2001). Construct validity in psychological tests. Psychological Bulletin, 52, 281-302.
[35] Hair Jr., J., Black, W., Babin, B. & Anderson, R. (2010). Multivariate Data Analysis: A Global Perspective. 7th Edition, Pearson Education, Upper Saddle River.
[36] Das, U., Ansari, M. & Ghosh, S. (2023). Measures of livelihoods and their effect on vulnerability of farmers to climate change: evidence from coastal and non-coastal regions in India. Environment, Development and Sustainability, 1-36.
[37] Ayamo, R. (2023). Contributions of sugarcane sharecropping to the smallholder farmers in Mayuge district (Doctoral dissertation, Makerere University).
[38] Kimkong, H., Promphakping, B., Hudson, H. & Day, S. (2023). Agricultural Transformation in the Rural Farmer Communities of Stung Chrey Bak, Kampong Chhnang Province, Cambodia. Agriculture, 13 (2), 308.
[39] Barrett, H. & Rose, D. (2022). Perceptions of the fourth agricultural revolution: What’s in, what’s out, and what consequences are anticipated? Sociologia Ruralis, 62 (2), 162-189.
[40] Vu, N. & Le, C. (2023). How much do cohesive and diversified networks improve financial access for small business? Applied Economics, 55 (4), 380-396.
[41] Girma, Y., Kuma, B. & Bedemo, A. (2023). Risk Aversion and Perception of Farmers on Endogenous Risks: An Empirical Study for Maize Producers in Awi Zone, Amhara Region of Ethiopia. Journal of Risk and Financial Management, 16 (2), 87.
[42] Castro, C., & Garcia, K. (2014). Default risk in agricultural lending, the effects of commodity price volatility and climate. Agricultural Finance Review.
[43] Corporate Finance Institute. (2022). Diversification. https://corporatefinanceinstitute. com/resources/ management/diversification/
[44] Gietzen, T., Yang, L., van Anrooy, R., Guinto, E., Badiola, J. & Das, P. K. (2022). Development of a credit and insurance programme for small-scale fisheries in the Philippines (Vol. 1244). Food & Agriculture Org.
[45] Ding, W. & Jin, W. (2023). Production operations, financing and information asymmetry in a supply chain with a random yield. Applied Economics, 1-21.
[46] Kaur, R., & Kaur, P. (2022). Diversion of Cooperative Loans in Rural Punjab. Journal (Online), 4 (1).
[47] Ntunzwenimana, J. (2018). Assessment of Factors Affecting Loan Diversion and Repayment Performance among Small Scale Farmers in Cibitoke, Burundi.
[48] Wangu, J., Mangnus, E. & van Westen, A. (2020). Limitations of inclusive agribusiness in contributing to food and nutrition security in a smallholder community. A case of mango initiative in Makueni County, Kenya. Sustainability, 12 (14), 5521.
[49] Ahmad, H. (2023). An assessment of factors determining loan repayment performance of SMEs in Gwarzo Local Government–a review. Journal of Global Economics and Business, 4 (12), 167-177.
[50] Ramashia, N. & Middelberg, S. (2022). Factors Influencing Agricultural Loan Repayments: The Case of Mafisa-Funded Farmers in Umkanyakude District Municipality. Technical Editing, 1181.
[51] Balchin, E. (2023). Farming in Transition in East Africa: Financial Risk Taking and Agricultural Intensification (Doctoral dissertation, University of Liverpool).
[52] Chaiya, C., Sikandar, S., Pinthong, P., Saqib, S. E., & Ali, N. (2023). The Impact of Formal Agricultural Credit on Farm Productivity and Its Utilization in Khyber Pakhtunkhwa, Pakistan. Sustainability, 15 (2), 1217.
[53] Ali, D. & Deininger, K. (2022). Institutional determinants of large land-based investments’ performance in Zambia: Does title enhance productivity and structural transformation? World Development, 157, 105932.
[54] Awunyo-Vitor, D., Wongnaa, C. & Aidoo, R. (2016). Resource use efficiency among maize farmers in Ghana. Agriculture & Food Security, 5 (1), 1-10.
[55] Udessa, F., Adugna, D. & Workalemahu, L. (2023). Socioeconomic Effects of Good Governance Practices in Urban Land Management: The Case of Lega Tafo Lega Dadi and Gelan Towns. Land, 12 (2), 369.
[56] Quaye, F., Nadolnyak, D. & Hartarska, V. (2017). Factors affecting farm loan delinquency in the Southeastern USA. Research in Applied Economics, 9 (4).
[57] Hepelwa, A. (2021). Potential of Fragmented Landholding on Crop Diversification and Credit Worthiness to Smallholder Farmers in Tanzania. African Journal of Economic Review, 9 (4), 238-252.
[58] Korthals Altes, W. (2019). Multiple land use planning for living places and investments spaces. European Planning Studies, 27 (6), 1146-1158.
[59] Kurien, A. (2022). Reliable Or not? Rethinking Shifting Cultivation Estimates to Inform Land-Use Policy. A Tradition in Transition, 220.
[60] Muruku, S. (2015). Factors influencing default in servicing agricultural loans: a case study of Agricultural Finance Corporation, Machakos County (Doctoral dissertation).
[61] Jumpah, E. T., Osei-Asare, Y., & Tetteh, E. K. (2019). Do farmer and credit specific characteristics matter in microfinance programmes’ participation? Evidence from smallholder farmers in Ada west and east districts. Agricultural Finance Review.
[62] Beygiharchegani, S., Makarov, U., Zhao, J. & Dwyer, D. (2018). Features of a Lifetime PD Model: Evidence from Public, Private, and Rated Firms: https://www.moodysanalytics.com/articles/2018/features-of-a-lifetime-pd-model
[63] Dutta, M. & Kashyap, P. (2018). What Determines Farmers’ Decision to Own Water Extracting Devices in Water Abundant Regions? A Study of Groundwater Markets in Assam.
[64] Bryan, J. (2023). Factors Affecting Syndicated Loan Spreads in Indonesia, Thailand, and Vietnam.
Cite This Article
  • APA Style

    M’Muruku Salesio Miriti, Gathungu Geofrey Kingori, Mwirigi Rael Nkatha. (2023). Influence of Enterprise Decision Making on Agribusiness Loans Default Rate in Agricultural Finance Corporation, Mount Kenya Region. International Journal of Economic Behavior and Organization, 11(2), 96-106. https://doi.org/10.11648/j.ijebo.20231102.16

    Copy | Download

    ACS Style

    M’Muruku Salesio Miriti; Gathungu Geofrey Kingori; Mwirigi Rael Nkatha. Influence of Enterprise Decision Making on Agribusiness Loans Default Rate in Agricultural Finance Corporation, Mount Kenya Region. Int. J. Econ. Behav. Organ. 2023, 11(2), 96-106. doi: 10.11648/j.ijebo.20231102.16

    Copy | Download

    AMA Style

    M’Muruku Salesio Miriti, Gathungu Geofrey Kingori, Mwirigi Rael Nkatha. Influence of Enterprise Decision Making on Agribusiness Loans Default Rate in Agricultural Finance Corporation, Mount Kenya Region. Int J Econ Behav Organ. 2023;11(2):96-106. doi: 10.11648/j.ijebo.20231102.16

    Copy | Download

  • @article{10.11648/j.ijebo.20231102.16,
      author = {M’Muruku Salesio Miriti and Gathungu Geofrey Kingori and Mwirigi Rael Nkatha},
      title = {Influence of Enterprise Decision Making on Agribusiness Loans Default Rate in Agricultural Finance Corporation, Mount Kenya Region},
      journal = {International Journal of Economic Behavior and Organization},
      volume = {11},
      number = {2},
      pages = {96-106},
      doi = {10.11648/j.ijebo.20231102.16},
      url = {https://doi.org/10.11648/j.ijebo.20231102.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijebo.20231102.16},
      abstract = {Farming projects funded using Agricultural Finance Corporation (AFC) capital are successful due to input of effective and efficient decisions. Farmer decisions have been observed to affect the loan default rate. The default rate for these loans has been reported to be 20.33%, which by standards is high since the yardstick for all types of loans in Kenya is 10%. This study aimed at establishing the influence of enterprise decision making on AFC loan default rate in Mount Kenya Region. Descriptive research design was used to study a population of 3,002 agribusiness borrowers in the 11-branch network region. Using systematic random sampling with an interval of 10, a sample of 300 respondents was obtained. Primary data on enterprise decision making was collected using a structured questionnaire. Statistical Packages for Social Sciences (SPSS V.27) and Stata version 15 was used to analyse data. To establish the effect of variables in estimating default rate, regression analysis was utilized. F-statistic was derived by performing ANOVA. The econometric model that was used to specify the statistical relationship between the independent variable and AFC loan default was binary logistic regression which showed that the all the four indicators of enterprise decision making that were used in the model explained 36.98% of AFC loan default rate. Results of the study revealed that agricultural enterprise diversification was significant at 5% while implementation of purposed project, land size and land use dynamics were significant at 10%, 5% and 1% levels of significance. Agricultural enterprise diversification and implementation of purposed project were found to have 7.6% and 6% associations with default respectively. In mitigation of default, borrowers should make decisions of using good agricultural practices of enterprise diversification and avoid diverting their loans to non-agribusiness projects. They should also make decisions on reasonable landholding which should be engaged in production while paying attention to dynamics of land use in regard to parcel purposes and consolidation. Farmers may utilize the output of this study to make effective and proficient decisions about good agricultural practices that are motivated by integration of credit into farming. The study recommends resource use-efficiency by encouraging borrowers to adopt land use and credit use strategies, use effective farming technologies, adopt risk mitigation through insurance schemes and form common interest groups to tap the dynamic externalities of grouping.},
     year = {2023}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Influence of Enterprise Decision Making on Agribusiness Loans Default Rate in Agricultural Finance Corporation, Mount Kenya Region
    AU  - M’Muruku Salesio Miriti
    AU  - Gathungu Geofrey Kingori
    AU  - Mwirigi Rael Nkatha
    Y1  - 2023/06/09
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ijebo.20231102.16
    DO  - 10.11648/j.ijebo.20231102.16
    T2  - International Journal of Economic Behavior and Organization
    JF  - International Journal of Economic Behavior and Organization
    JO  - International Journal of Economic Behavior and Organization
    SP  - 96
    EP  - 106
    PB  - Science Publishing Group
    SN  - 2328-7616
    UR  - https://doi.org/10.11648/j.ijebo.20231102.16
    AB  - Farming projects funded using Agricultural Finance Corporation (AFC) capital are successful due to input of effective and efficient decisions. Farmer decisions have been observed to affect the loan default rate. The default rate for these loans has been reported to be 20.33%, which by standards is high since the yardstick for all types of loans in Kenya is 10%. This study aimed at establishing the influence of enterprise decision making on AFC loan default rate in Mount Kenya Region. Descriptive research design was used to study a population of 3,002 agribusiness borrowers in the 11-branch network region. Using systematic random sampling with an interval of 10, a sample of 300 respondents was obtained. Primary data on enterprise decision making was collected using a structured questionnaire. Statistical Packages for Social Sciences (SPSS V.27) and Stata version 15 was used to analyse data. To establish the effect of variables in estimating default rate, regression analysis was utilized. F-statistic was derived by performing ANOVA. The econometric model that was used to specify the statistical relationship between the independent variable and AFC loan default was binary logistic regression which showed that the all the four indicators of enterprise decision making that were used in the model explained 36.98% of AFC loan default rate. Results of the study revealed that agricultural enterprise diversification was significant at 5% while implementation of purposed project, land size and land use dynamics were significant at 10%, 5% and 1% levels of significance. Agricultural enterprise diversification and implementation of purposed project were found to have 7.6% and 6% associations with default respectively. In mitigation of default, borrowers should make decisions of using good agricultural practices of enterprise diversification and avoid diverting their loans to non-agribusiness projects. They should also make decisions on reasonable landholding which should be engaged in production while paying attention to dynamics of land use in regard to parcel purposes and consolidation. Farmers may utilize the output of this study to make effective and proficient decisions about good agricultural practices that are motivated by integration of credit into farming. The study recommends resource use-efficiency by encouraging borrowers to adopt land use and credit use strategies, use effective farming technologies, adopt risk mitigation through insurance schemes and form common interest groups to tap the dynamic externalities of grouping.
    VL  - 11
    IS  - 2
    ER  - 

    Copy | Download

Author Information
  • Department of AGEC, AGBM & AGED, Chuka University, Chuka, Kenya

  • Department of Plant Sciences, Chuka University, Chuka, Kenya

  • Department of Business Administration, Chuka University, Chuka, Kenya

  • Sections