Smallholder farmers are the center of the agricultural transformation in Kenya as they play a critical role in GDP contribution, employment creation and the general food security of the country. Their role in the agricultural supply chain remains underutilized based on the limitation in the available resources at their disposal (Omulo and Kumeh, 2020). It therefore becomes difficult for their transition from subsistence to a commercial agriculture. The focus on smallholder farmers is based on the fact that smallholders have scarcity of resources as well as inefficient technologies that limit their transformation into commercial farming (Hakizimana et al., 2017).

Whether the investment in smallholder farmers is supported or questioned, the fact that these farmers face several challenges is acknowledged by both sides. Actually, they are these challenges which reinforce the pessimistic views  of  focusing  on  the  transformation  of agriculture among smallholder farmers. The point is that agriculture-based growth necessitates the pumping of capital which is a challenge to many farmers due to low income and the lack of collaterals to secure loans (Musembi, 2019). The perishability nature of agricultural products makes securing loans to finance agriculture a nightmare by many small-scale farmers.

Access to credit in Kenya has been identified as an inhibiting factor towards agricultural commercialization. High interest rates from banks and other financial lenders have made access to capital a challenge especially when focusing on the perishability of agricultural products (Ng’etich, 2019). While revolving funds may be available among different farmer groups, the rate of credit access by many farmers in the country is still low.

Whether real or perceived, new technologies among small holder farmers are slow to disseminate (Mungai and Sma, 2019). Revolving Funds (loans) have been implemented by various countries in trying to address their effectiveness, accrued benefits and improving livelihoods. The adoption of these loans and their intensity of usage among farmers has scanty documentation especially in Kenya. However, literature concurrently reveals that adoption is a successive development as opposed to a simultaneous event.

The first step is assumed that a farmer chooses to deliberate on particular attributes about technology. Subsequently, the second step which is the outcome is manifested in the intensity of adoption (Khatri-Chhetri et al., 2017). The main assumption is that a smallholder farmer will choose and adopt new technologies that he/she perceives that has minimum risk. Further, smallholder socioeconomic characteristics such as the farmer’s experience are perceived to determine technology adoption over and above the extent of participation level (Ali et al., 2018).

A cross-sectional study was done in Chepalungu, Bomet County in Kenya, where data employed was gathered between September and   November,   2019.   The   study   used    probability   sampling technique to select Chepalungu sub-county due to its wide coverage with the main economic activity being dairy production. It has five wards which include: Kong’asis, Siongiroi, Sigor, Chebunyo and Nyangores. Smallholder dairy farmers were sampled using purposive sampling technique from all the five wards. The sample size required was determined using the following formula by Kothari (2004).

n=Z²pq/e²                                                                                       (1)                                         

where n is the required sample size, Z is the confidence level = 1.96 at 0.05 level of significance, p is the proportion of the population represented, q is the proportion of the population not represented, q = 1-p and ? = 0.05, is the acceptable margin of error for the proportion estimated. Hence,

n = (1.96)² 0.75 (0.25) / (0.05)² = 288.

There were 1,384 members that had benefited from the Revolving Funds according to the list of beneficiaries in Chepalungu sub-county office (CFSP, 2018). Out of this list, 308 were from Kong’asis, 346 (Siongiroi), 327 (Chebunyo), 211 (Nyangores) and 192 from Sigor. The required sample per ward was then determined using proportionate to size sampling method by Kothari (2004).

n_i = n / N × P_total                                                                                     (2)

where, ??= Desired sample size per ward, ? = Desired sample size for the study, ? = Total number of the target population in the sub-county, and ??????= population per ward. The results are presented in Table 1.

Equal number of respondents from the non-beneficiary group was chosen. This comprised 144 beneficiaries and 144 non-beneficiaries of Revolving Funds. Semi-structured questionnaires and interview schedules were used to collect primary data. The questions were administered to dairy farmers who were beneficiaries and non-beneficiaries of revolving funds in Chepalungu sub-county to yield a sample size of 288 dairy farmers.

A double-hurdle model was applied to determine household’s decision to take part in revolving funds and the intensity of revolving funds in acquisition of dairy inputs. The Double-Hurdle (DH) or two-tiered model was proposed in 1971 by Cragg and it allows for the possibility of having different set of factors influencing farm input participation and its level (Ingabire, 2019). With DH model, a farmer is assumed to pass into two distinct hurdles in the process of farm input participation. In the first hurdle, a probit regression is estimated to determine the factors affecting credit participation (decision to have revolving funds). In the second tier, a truncated normal regression model (Tobit) is used over the non-zero values of the sampled observations (those who acquired the revolving funds), to identify the differential in the usage of the revolving funds.

The first stage of the model involves selection and it is determined using a probit model. This represents the likelihood of confined reflection.

 where  = Participation determinant, z = explanatory variables speculated to determine participation, α= a vector of parameters, and ε = error term.

The last hurdle postulates the resultant effects of acquired credit. It requires the application of a truncated model to ascertain the magnitude of participation in credit (Revolving Funds) for the acquisition of inputs. Only households that recorded a positive value in the use of credit will be considered. The model is specified as:

where  = remarkable reaction on the extent of credit use through dairy inputs, x = explanatory variables speculated to determine the extent of credit use, β = a vector of parameters, and  µ = error term.

Descriptive statistics

Gender

The ratio of the sampled farmers indicate that male were 46.53% while female household heads were 53.47%. Household head position is regarded as crucial in making decisions and the extent of  technology  adoption. Furthermore, gender also contributes a great deal of information about the conduct of the people dwelling in the area of study (Gebre et al., 2019).  The basic resources such as land and livestock are owned and controlled by men and this has a central part of program planning, design and implementation of a breeding program. Therefore, the preference of the household heads was likely to affect their participation in the credit acquisition through the revolving funds (Figure 1).

Activities engaged

The largest share of the respondents indicated that they engaged in business and other income earning activities while farming took 21.9% of the reported share. Most farmers in Kenya diversify their means of earning a living in order to  survive  during  economic  recessions  and  to curb poverty. This is highly practiced by the rural communities where agriculture is the main occupation (Tomich et al., 2018) (Figure 2).

Feeding routine

Almost all farmers in the study area reported an open grazing as opposed to zero grazing. Any rational farmer will adopt a farming routine that increases returns to resources that are constrained.  The minimum land size threshold to indicate factor constraint was perhaps not yet reached unlike in high potential areas where the land sizes are small and ideal for zero grazing.  The land size from the sampled area was not a constraint towards dairy farming and therefore there was free land that could facilitate an open grazing mechanism (Figure 3).

Regression analysis

As double hurdle model maximum likelihood estimates result show, Chi square overall = 16.50, P overall ≥.0113 is significant at 5% level. This indicates that both stages taken together significantly explain the data and the joint significance of the explanatory variables that were used. The results of the first tier depicted that the years of farming experience and the type of dairy enterprise, significantly influenced decision to participate in revolving funds at 10 and 1%, respectively as indicated in Table 1.

Experience in dairy farming influences (P ≥0.079) the decision to participate in revolving funds by 14.2%. An increase in experience of the dairy farmers by one year increased the probability of engaging in revolving funds as indicated in Table 2. This means that farmers with more experience in dairy farming have a higher probability of engaging in revolving than those with less experience because, they have more knowledge and skills. Our findings are supported by the findings of Doss (2018) who found out that an increase in farmers experience by one year, increased probability of increasing the output of a given farm enterprise. This is attributed by the fact that more skills and knowledge are acquired in the process of production.

The study provides new insights on the adoption of technologies among small holder farmers. As the results indicate, once the hurdles have been accounted for, a change  in   dairy  enterprise  from  one  level  to  another decreases the amount of revolving funds used in the purchase of inputs (dy/dx=0.66, p≥0.007). Similarly, an increase in one year of farming experience increases the likelihood of revolving fund usage in farm inputs by 14.2%. Whereas credit access has hindered agricultural commercialization, revolving funds across farmers’ groups have not been widely adopted especially by the dairy farmers in Bomet County. There was a sharp discrepancy between the willingness to use credit and the actual absorption into input purchases. There is therefore need for the stakeholders to help address the challenges associated with the use of revolving funds among the dairy farmers. Stringent measures on the intensity of credit use should also be a focus as many farmers reported low absorption of the credit into dairy input purchases.

The authors have not declared any conflict of interests.

The authors thank the Centre of Excellence in Sustainable Agriculture and Agribusiness Management (CESAAM), African Economic Research Consortium (AERC) and Department of Agribusiness-County Government of Bomet.