Abstract

Moderating the indicators of default in agribusiness loans presents shifts in the performance of credit markets. Mount Kenya region of Agricultural Finance Corporation (AFC) registered a high default rate of 20.33% in repayment of agribusiness loans, attributable to the moderator effect, inter alia. The comparison of 20.33% with a 10% benchmark set by Central Bank of Kenya for all types of loans is unfavourable. This study aimed at analysing the effects of the moderator (extraneous shocks) on the relationship between the borrower-lender determinants of default and the Agribusiness Loan Default Rate (ALDR), in AFC Mount Kenya region. Using a descriptive research design in the region with 11 branches and a population of 3,002 agribusiness borrowers, 300 respondents was drawn as sample, using a systematic random sampling technique with an interval of ten borrowers. Primary data was collected using a structured questionnaire and analysed using Statistical Package for Social Sciences (SPSS V.27). Use of stepwise econometric regression model on relationship between the borrower-lender indicators and ALDR revealed as follows: borrower’s socioeconomic indicators and the extraneous shocks, with introduction of the moderator, there was 2.8% increase in the prediction power; for enterprise decision making indicators there was 0.1% increase in ALDR; and in lender behavioural characteristics indicators, a 3.7% increase in the prediction power was registered. The ANOVA Analysis on the overall significance indicated the existence of a statistically and significant interaction between respective indicators and the extraneous shocks on the ALDR (F=37.988, F=29.659, F=72.092 and F=56.887; p-value=0.00<0.05). The study contributes to the existing body of knowledge in risk management and agricultural finance by accentuating the far-reaching threats of extraneous shocks in constraining the sustainable production process, thus ALDR. The study recommends that it is strategically imperative to close the risk gaps facing the farming communities thus: players to adopt coping strategies- interventionist policies pointing to partnerships for training, facilitating access to effective technologies and subsidizing relevant agricultural insurance to affordability; besides, policy directions should encourage proactive engagement in good agricultural practices and ubiquitous risk mitigation; borrowers should be proactive and alert by providing for contingencies and eventually absorbing agro-risks; penultimately, lenders should be collaboratively engaged in supervised lending to ensure timely interventions and individualized involvement of borrowers, thus compatible with contemporary agribusiness realities; ultimately, credit stakeholders should craft advocacy and inclusion mechanisms, aiming to cushion the societies from incipient distresses and devastating constraints.

1. Introduction

The fortunes of the farming communities have been profoundly transformed by agribusiness; the contemporary realities associate agri-based enterprises with smart modern farming practices which are efficient to ameliorate the farming way of life and address the environment concerns ¹. Agribusiness represents both commercial value and creation of working opportunities, attracting many Kenyans due to its strategic importance and the abundant endowment with natural resources ². In Mount Kenya region, agribusiness accommodates the diversity of burgeoning population, thus meeting the demands, tastes and preferences ³.

The growth of the farm sector is supported by mandating and empowering the AFC as a channel for the government’s public policy intervention in providing affordable loans to farmers at 10% interest rate ⁴. Agribusiness loans play an integral part in modern farming by mobilizing its inherent productive capacity, thus empowering farmers ⁵. Credit enhances purchase of farm inputs like seeds, spraying chemicals, manure and fertilizers; finances costs of weeding, harvesting, storage and transport to deliver the produce to market especially when other income sources fail ⁶. Through credit support function, more players intervene in solving agribusiness liquidity constraints so as to enhance access of high-return investments by the beneficiaries ⁷.

Despite the strategic importance of the farm sector and the policy interventions in enhancing access to affordable loans, AFC Mount Kenya region still experiences financial difficulties, negative growth and exit rate due to high default rate of 24.15% which compares unfavourably to 10% benchmark for all loans in Kenya ⁸. Default in servicing of loans is a global, regional and national problem triggering a fiasco in suitable lending and reliable policies of credit ⁹. It has longstanding outcomes in the sustainability of agribusiness enterprises. Further, credit risk elicits concerns to global financiers because it has evolved to majorly induce default problem and create disturbances in ultimate lending goals ¹⁰. Loan default is of critical concern due to lowering cash flows, depressing liquidity and distressing finances ¹¹.

The purpose of this study is to dissect the effects of the moderator (extraneous shocks) on the relationship between borrower-lender determinants and the ALDR, in AFC Mount Kenya region. The determinants are justified thus: a study conducted by Adusei ¹² established that there are four main determinants of loan default namely: the borrower, enterprise, lender and loan. These determinants make up the two sides of the credit model, which is the borrower side, and lender side ¹³. Borrowers and their enterprises implemented using borrowed funds make up the borrower side ¹⁴. Specifically, this side is comprised of borrower’s socio-economic characteristics and enterprise decision characteristics ¹⁵. The lending institution and the loans they disburse make up lender side determinants ¹⁶. This phenomenon qualifies the lender behavioural characteristics ¹⁷. This study therefore adopted three independent variables namely: borrower socio-economic profile, enterprise decision making and lender behavioural characteristics.

On top of this two-sided determinants, macroeconomic and climatic variables have been reported to influence default in agribusiness loans ¹⁸. This is the moderating variable whose inclusion widens the scope of default analysis in farm loans as suggested by extant research ¹⁹. Production/environmental and marketing risks have been identified as extraneous shocks that moderate the repayment of farm loans ²⁰. Production risks emanate from agro-climatic extremes which result to bad weather such as drought and flood; and biological hazards such as ravenous pests and diseases like covid -19 ²¹. This study therefore adopted extraneous shocks as the moderator. In the relationship, the moderator is the third variable (Z) in addition to the independent or predictor (X) and the dependent or outcome (Y) variables, thereby influencing the outcome by affecting the strength and/or direction of the relation between the two variables ²². The conceptualization of this study demonstrates the relationship between the moderating variable and the independent variables to influence the outcome of the dependent variable (Figure 1).

The framework presents the interrelationships that were established among the main components of the study. The independent variables are borrower socio-economic profile, enterprise decision making and lender behavioural characteristics. AFC agribusiness loan default rate (ALDR) is the dependent variable. The relationship between independent and the dependent variable is moderated by and extraneous factors. The borrower socio-economic profile is indicated by Farming experience, Borrowing experience, Off -farm income, Multiple borrowing and Borrower-lender distance. The indicators of enterprise decision making include: Agricultural enterprise diversification, Implementation of purposed project, Land size and Land use dynamics. lender behavioural characteristics variable has such indicators as: Farm visit, Disbursement timeliness, Political lending and Adequate funding. The extraneous shocks moderating variable is indicated by: Agroclimatic extremes, Market volatility and biological hazards (Figure 1).

Some extant literature suggests the importance of including a moderator on the relationship between the study variables. The moderating effect adopted in studies was guided by directions suggested by Baron ²³. A study by Chowdhury ²⁴ on natural disasters and corporate default risk, showed the effect of moderators to the relationship between independent and dependent variables. The indicators of moderator were financial access, firm’s debt capacity and operational risk. Regression results on the moderating effect of financial access, firm’s debt capacity and operational risk on the association between firm’s exposure to natural disaster intensity and default risk suggested that there was moderation. Chi ²⁵ observed that credit risks emanating from shocks and lender environment affected the lending decisions and the subsequent repayment of loans.

Idris ²⁶ used the moderator to establish the moderating role of loan monitoring on the relationship between macroeconomic variables and NPLs and reported that banks managements needed to pay attention to loan monitoring activities by incorporating it into key performance indicators. Mutlu ²⁷ studied to reveal the moderator effect of financial literacy on locus of control and financial behaviour and established that financial literacy plays a significant role as a moderator variable that interacts with locus of control. Shatnawi ²⁸ studied moderating effect of enterprise risk management on the relationship between board structures and corporate performance. The study established that enterprise risk management had the potential of moderating board structures against corporate performance.

Zebal ²⁹ empirical study showed that market turbulence and competitive intensity have a moderating effect on the association between market orientation, monetary performance and customer satisfaction of 364 private banks in Bangladesh. Shehata ³⁰ analysed the moderating role of perceived risks in the relationship between financial knowledge and the intention to invest in the Saudi Arabian stock market. The perceived risks were entered as moderating variable. The perceived risks negatively moderated the relationship between financial knowledge and intention to invest, thus the lowering the perceived risk and making the relationship to be strong. There was a positive relationship between financial knowledge and intention to invest and also between perceived risks and intention to invest and that perceived risks moderated the relationship between financial knowledge and intention to invest in the Saudi Arabian Stock Market.

Fahlenbrach ³¹ argued that the impact of a sudden negative cashflow shock was less negative on firms with greater debt capacity. Although large firms had a higher level of financial access, they were still unable to absorb more debt if they had lower debt capacity. Taken together, firms with a higher debt capacity were more capable of absorbing new debts during a liquidity shock and are therefore subject to the lower exposure to default risk than firms with lower debt capacity. Simiyu ³² studied project management practices and performance of agricultural projects by community-based organizations in Bungoma County, Kenya. The study observed that environmental enablers (moderating variable) were found to have an influence on the relationship between project management practices and project performance. The study additionally evaluated the moderating role of environment enablers on the relationship between project management practices and agricultural project performance. Simiyu’s study did not cover agroclimatic extremes and biological hazards, thus exhibiting conceptual gaps. All the reviewed studies have contextual, conceptual and methodological gaps, which the current study attempts to close.

2. Research Methodology

Mount Kenya region was selected through convenience sampling because of good branch network, variety of agribusiness activities and agroclimatic zones. The study was conducted between June 2022 and December 2022 in the designated region, which is one of the AFC catchment areas within the country. The Global Positioning System (GPS) coordinates of this region are 36.561, 2.168 and 37. 852, -0.85 ³³. The branch network of this region comprises of 11 branches which includes Meru, Chogoria, Embu, Kerugoya, Thika, Murang’a, Nyahururu, Maralal, Nanyuki, Nyeri and Karatina. These branches are spread in the 9 counties which include Meru, Tharaka-Nithi, Embu, Kirinyaga, Kiambu, Murang’a, Samburu, Laikipia and Nyeri ³⁴.

The study used descriptive research design which is a tool aimed at systematically obtaining information so as to be in a position of describing a phenomenon, a situation, or population ³⁵. The design utilizes a myriad of research methods to explore the variables in question by chiefly employing quantitative data for descriptive purposes, collection and analysis of numerical data ³⁶. This design was accurate and systematic in collecting and describing AFC agribusiness borrowers in Mount Kenya region, the default problem and the determinants of default. The choice of this design was guided by the possibility of using diverse methods of research to examine, observe and measure variables which concern default in agribusiness loans in AFC.

The population of study was farmers who have borrowed agribusiness loans from the 11 branches of Mount Kenya region for the period 2018/2022. All agribusiness loans disbursed by AFC are serviced in 36 months. These borrowers comprise of all current beneficiaries without regard to their loan level and repayment performance. The AFC, 2022 branch reports show the distribution and performance of borrowers who are servicing 3-year agribusiness loans total to 3,002, the apportionment of respondents and average default in the 11 AFC branches of Mount Kenya region (Table 1).

The population was 3,002 borrowers from which sampling was designed to identify the default cases and non-default cases (controls). Default was for all those with loans whose repayments were not regularized regardless of cause of noncompliance. The controls were selected based on absence of history of nonconformity. From AFC records as per close of year 2022, there were 3,002 agribusiness borrowers in Mount Kenya region. Using systematic random sampling method with a ‘skip’ of ten, a sample of 300 borrowers was obtained (Table 1).

The interval was used to avoid clustered selection, thus ensuring that respondents were spread across the branches under study. By “skipping” at the interval of 10, overconcentration in one branch was eliminated, thus fair distribution which guaranteed representativeness. The interval guarantees that the sample is drawn from both defaulters and non-defaulters ³⁷. The calculation for interval is as follows: k=N/n, where k, is the sampling interval (skip), n is the sample size, and N is the population size. In our case the sampling interval was determined thus: k= 3,002/300 = 10. This means that, the respondents were selected from AFC list at random after skipping ten.

It was important to select a sample to represent population from a comparatively similar population. Stratification aims to reduce the standard error by providing some control over variance. To calculate the size of the sample Daniel ³⁸ formula was used as follows:

where;

= sample size,

= Z- statistic for a level of confidence,

= expected default or proportion (in proportion of one; if 24.15%, = 0.2415), and

= precision (in proportion of one; if 5%, = 0.05).

Z-statistic (Z): For the level of confidence of 95%, which is conventional, Z value is 1.96. This means that, investigators present their results with 95% confidence intervals (CI). Using 1.96 as the standard deviation for 95% level of confidence, with known sample size and a known proportion, precision can be determined. To calculate the margin of error, the non-negative square root is considered ³⁹. In our case, there were 3,002 agribusiness loan beneficiaries. Expected default was 24.15% of the total beneficiaries. To work out the sample size, the following calculation was done:

=confidence level =1.96

= Default =0.2415

=precision =0.04843

= 300. The distribution of these 300 respondents as per branch is indicated in Table 1.

The study employed a questionnaire which was tailored keenly and thoroughly to ensure that all relevant material facts were captured. This was to ensure accuracy and accommodation of all pertinent details. The structured questionnaire was pilot tested in Central rift region at Nakuru, Kericho, Molo and Naivasha branches. This established its relevance to the study producing accurate results.

Reliability of the research instrument (questionnaire) was evaluated using Cronbach Coefficient Alpha. Evaluation of questionnaire was done by estimating the internal consistency of responses so as to examine the reliability of scales. Cronbach’s alpha is appropriate for dichotomous variables coded as 0 or 1 ⁴⁰. Zero means that there is no internal consistency between items in the questionnaire while one means that internal consistency is perfect ⁴¹. A higher value of greater than 0.9 indicates excellent quality, while a lower Cronbach's Alpha of less than 0.5 indicates unacceptable quality ⁴². This study found that the questionnaire was reliable since the scale reliability coefficient was 0.7318>0.7 which is the acceptable scale. This is because the Cronbach's Alpha values for different dimensions of the present study are more than 0.7, meaning the data are taken as sufficiently reliable and consistent (Table 2).

George ⁴³ provided that the scale reliability coefficient of any research instrument should be greater than 0.7 for it to be deemed acceptable and reliable. This observation was supported by Hair ⁴⁴ who agreed that the value of more than 0.7 in Cronbach's Alpha indicates that collected data was sufficiently reliable and consistent.

This study used both primary and secondary data as sources of information. All questions from the five sections of the questionnaire were used to collect quantitative data where borrowers provided answers regarding their socio-economic profile, their decision making about enterprises, their lender behavioural characteristics and the extraneous shocks which catch up with them as the project cycle progresses. Primary data was obtained from the respondents who were current beneficiaries of AFC farm loan. The secondary data that was collected included data on performance of branches, type of loan products, administrative units and agribusiness activities.

The software for analysis was Statistical Packages for Social Sciences (SPSS V. 27.0). The output from quantitative data was given in descriptive statistics and regression analysis. Regression analysis was used to describe the relationship between independent and dependent variables. The econometric models used was stepwise regression model. Chi-square was used to make comparison of consistency between expected and actual results so as to establish the extent of the divergences. ANOVA results were obtained in the regression analysis using SPSS. After running ANOVA test, the F-statistic was obtained so as to test for the adequacy of the regression model.

A number of regression models was used depending on the objective intended to be achieved.

This objective will be achieved by modelling using stepwise regression as proposed by ²³. The regression for moderation in this study was formulated as follows:

Where;

= AFC default rate; = Constant − = Regression Coefficients; = Error term; is the borrower socioeconomic indicators, is extraneous shocks, and is the interactive term.

3. Results and Discussion

The study identified the AFC loan default in different branches of Mt. Kenya region. Results indicated that the total default rate for AFC Mount Kenya region was 20.3% while the compliance rate was 79.7%. Out of the 300 borrowers who were interviewed, it was observed that 239 (79.7%.) complied in repayment of agribusiness loans, while 61 (20.3%) borrowers defaulted in loan servicing (Table 3).

In calculation of the dependent variable which is AFC loan default, logistic regression model was used. The probability of default takes values of 1 for default and 0 for compliance. To provide the logistic estimates of the coefficients for the different independent variables, the calculation is a shown. The model was tested at 5% level of significance (p-values = 0.00<0.05). The logistic estimates revealed that the dependent variable (AFC loan default rate) was significant (p-values = 0.02<0.05). The findings revealed that borrower socio-economic profile and lender behavioural characteristics were also significant (p-values = 0.00<0.05) while enterprise decision making was insignificant (p-values = 0.52>0.05).

In this objective step wise regression model was adopted to assess how extraneous shocks affected the interactions of the lender and borrower’s determinants and the loan default rate. The extraneous shocks considered in the study include agroclimatic extremes, market volatility and biological hazards effects. An interactive term (M) was first obtained by getting the product of the one of the determinants with the moderator variable which was the extraneous shocks. Zeyang ⁴⁵ used a stepwise regression to conduct an empirical analysis on data in the analysis of the risk of corporate debt default. The study also relates to that of Idris ²⁶ used the moderator to establish the moderating role of loan monitoring on the relationship between macroeconomic variables and NPLs.

Results show that borrower’s socioeconomic indicators and the extraneous shocks explained R-squared (R²) of 0.248 (24.8%) which meant the variation in the AFC loan default rate. The adjusted R² (0.243) explained 24.3% variation in AFC loan default rate. Since it is close to R², there is implication that there were no significant outliers. This relationship is significant at p-value=0.000<0.01 (F-value=48.908) Table 4.

Introduction of the moderating variable made the Adjusted R-squared improve to 27.1% which is 2.8% increase in the prediction power. This implies that the second model proved to be better predictor of AFC loan default rate (Table 4). In the borrower socioeconomic profile, the interactive term was obtained by the product of socio-economic indicators and the presence of extraneous shocks. Stepwise regression model provided the results that showed the relationship in the presence and the absence of the interactive term on the loan default rate. This relates to study by Mutlu ²⁷ about the moderator effect of financial literacy on locus of control and financial behaviour; to obtain the moderation of internal locus of control, the interaction variable was created by multiplying financial literacy and locus of control in the data set as advised by ²³.

The ANOVA Analysis on the overall significance indicates that borrower socio-economic profile has significant effect on AFC loan default rate. This is depicted by F-statistic of 37.988 and a p-value of 0.000 which is less than 5% level of significance. The model showed that a statistically and significant interaction existed between socio-economic indicators and the extraneous shocks and the AFC loan default rate (F=37.988; p-value=0.00<0.05) Table 5.

The coefficients for the individual significance for socio-economic indicators showed that borrower’s socio-economic indicators had a negative statistically significant relationship at 5% with the AFC loan default rate (β= -0.023, p-value = 0.02<0.05) Table 6.

Results imply that more years of farming experience, borrowing experience and more off farm income among the borrowers the less the rate of default rate. Extraneous shocks demonstrated positive and statistically significant relationship with the rate of loan default among borrowers. (β=0.215, p-value=.000). The interactive term (product of extraneous shocks and socioeconomic indicators) had negative statistical linear relationship with the AFC loan default rate. (β= -0.034, p=0.000). The model findings imply that extraneous shocks may negatively affect the borrower’s socioeconomic profile and the AFC default rate relations. This relates to the findings of Zebal ²⁹ who observed that formal marketing education was statistically significant and negatively related to market orientation. The following regression model defines how the various variables involved relate:

Y=0.110-0.023X+0.215Z-0.034M.

where:

Y is the AFC loan default rate, X is the borrower socioeconomic indicators, Z is extraneous shocks, and M is the interactive term.

On the enterprise decision making among the borrowers, the interactive term was formed by the multiplication of the enterprise decision making with the presence of the extraneous shocks. The regression model showed the summary of the relationship with and without the interactive term on the AFC loan default Table 7.

The results show that enterprise decision making and the presence of extraneous shocks explained 22.2% of the variation in the ALDR. This relationship is significant at p-value =0.000<0.05 (F-value=43.640). However, with the introduction of the interactive term the Adjusted R squared increased to 22.3% which 0.1% increase Table 7. This study found that extraneous shocks affected the outcome of borrower’s enterprises, thereby resulting to loan repayment difficulties. This finding relates to those of Shatnawi ²⁸ who reported that moderating the enterprise risk management improved the responsiveness in a firm thereby improving the decision-making process and increasing the revenue streams.

The ANOVA Analysis on the overall significance indicates that in enterprise decision making the two models had statistically significant effect on AFC loan default rate (F=29.659; p-value=0.00<0.05) Table 7. The coefficients for individual significance are shown hereunder Table 8.

Enterprise decision making had no statistically significant relationship with the borrower’s loan default rate (β= 0.016, p-value=0.260>0.05). The findings implies that enterprise decision making of the borrower either through diversification, land use dynamics and project implementation did not statistically affect the AFC loan default rate. However, extraneous shocks were positively and statistically significant in relationship with the rate of loan default among borrowers. (β=0.229, p-value=0.00<0.05). The interactive variable made by the multiplication of enterprise decision making of the borrowers and the extraneous shocks had no significant relationship with the AFC loan default rate. (β= -0.014 p=0.217>0.05) Table 9.

The model findings implies that extraneous shocks statistically affect the borrower’s enterprise decision making like in the diversification of agribusiness portfolios and the AFC loan default rate relationship. These findings relate to those of Fahlenbrach ³¹ who reported that there was statistically significant relation between past payouts and stock returns during the collapse period (period of extraneous shocks), such as during the covid-19 era.

The regression model is given as follows:

Y=0.279+0.016X+0.229Z-0.014M

Where;

Y is the AFC loan default rate; X is the enterprise decision making of the borrower and M is the interactive term.

For the lender behavioural characteristics, the interactive term was formed by the multiplication of the indicators of lender behavioural characteristics made by the AFC officials to the borrowers and the presence of the extraneous shocks. In a stepwise regression model, lender behavioural characteristics increased AFC loan default rate as obtained both with and without the interactive term. The summary of moderating effect of extraneous shocks on the lender behavioural characteristics and AFC loan default rate Table 10.

The results show that the lender behavioural characteristics and the presence of extraneous shocks explained 32.2% of the variation in the AFC loan default rate. This relationship is significant at p-value =0.000<0.05 (F-value=72.092). However, with the introduction of the moderating variable the Adjusted R-squared improved to 35.9% which is 3.7% increase in the prediction power. This implies that the model with the interactive term is the best predictor of AFC loan default rate Table 11.

ANOVA Analysis gave results that showed that the two models were all significant (F=72.092; p-value=0.00<0.05) and (F=56.887; p-value=0.00<0.05). This shows that there was a statistically significant interaction existing between lender behavioural characteristics, extraneous shocks and the AFC loan default rate Table 11.

The coefficients for individual significance gave regression results that showed that lender behavioural characteristics by the AFC officials had a positive statistically significant relationship with the borrower’s loan default rate (β=0.071, p-value=0.00<0.05). The results may imply that the greater the number of farm visits made by the AFC officials to the borrowers led to a notable decrease in the rate of AFC loan default. Extraneous shocks had a positive statistically significant relationship with the rate of loan default among borrowers. (β=0.212, p-value=0.00<0.05). The interactive variable had a positive statistically significant relationship with the AFC loan default rate. (β=-0.053, p=0.00<0.05) Table 12.

These findings relate with those of Shehata ³⁰ who reported that the perceived risks had a significant positive effect on investment efficiency and on the investors’ investment intentions. Extraneous shocks may positively affect the lender behavioural characteristics like the number of farm visits and the AFC loan default rate relationship. These findings relate with those of Chi ²⁵ who noted that an increase in operational risk (shocks) can cause lenders operations to fluctuate, making their financial situations more unstable and in turn affecting borrowers’ repayment abilities.

The regression model is given as follows:

Y=0.479+0.071X+0.212Z+0.053M

where: Y is the AFC loan default rate; X is the lender behavioural characteristics and M is the interactive term.

4. Conclusion

The study established that extraneous shocks influence the relationship between independent and dependent variables. For instance, there was a statistically significant interaction that exists between socio-economic indicators and the extraneous shocks and the AFC loan default rate (F=37.988; p-value=0.00<0.05); there was no statistical significance with interaction that exists between enterprise decision making of the borrower, extraneous shocks and the AFC loan default rate (p-value=0.216>0.05) which means that extraneous shocks do not statistically affect the borrower’s enterprise decision making. Lastly, there was a statistically significant interaction that exists between lender behavioural characteristics, extraneous shocks and the AFC loan default rate (F=56.887; p-value=0.00<0.05). The interactive variable had a positive statistically significant relationship with the AFC loan default rate. (β=-0.751, p=0.000). The model findings indicates that extraneous shocks may positively affect the lender behavioural characteristics and AFC loan default rate relationship. These findings show that the extent of default can be tackled if loan stakeholders work collaboratively to exterminate the extraneous shocks. The study recommends that efficient decision making by loan players is important and convenient mileage to avert risks emanating from agro-shocks; as such, borrowers should be proactive and alert by providing for contingencies and eventually absorbing agro-risks; also, lenders should be collaboratively engaged in supervised lending to ensure timely interventions and individualized involvement of borrowers so as to be compatible with contemporary agribusiness realities; ultimately, credit stakeholders should craft advocacy and inclusion mechanisms, aiming to cushion the societies from incipient distresses and devastating constraints. The study recommends that it is strategically imperative to close the risk gaps, facing the farming communities, by players adopting coping strategies: interventionist policies should point to partnerships for training, facilitating access to effective technologies, subsidizing relevant agricultural insurance to affordability; besides, policy directions should encourage proactive engagement in good agricultural practices and ubiquitous risk mitigation. The study contributes in illuminating on the significance of agricultural finance in accelerating growth and sustainability of agribusiness and advising on strategic priorities that should be adopted in risk management so as to deal a blow to extraneous shocks.

ACKNOWLEDGEMENTS

I would like to sincerely appreciate Chuka University for facilitating the furtherance of this research and AFC management and farming borrowers for their support.

Competing Interests

The authors have not declared any competing interests.

References