Agricultural extension services typically include capacity development through training, strengthening innovation processes, building linkages between farmers and other agencies, and helping to strengthen farmers' bargaining position through appropriate institutional and organisational development (Sulaiman and Hall, 2002). This form of extension service is common in Zimbabwe and is funded largely by the treasury with supplementary funding from donors for specific agricultural programmes (Saravanan, 2008; Oladele, 2011).

 

The public component of Zimbabwe’s extension system is delivered by the Department of Agricultural, Technical and Extension Services (AGRITEX). AGRITEX is the largest public rural intervention agency in Zimbabwe with representatives at the national, provincial, district and village levels (IFPRI, n.d.). Sharp reductions in tax revenue and donor funding that followed the introduction of Zimbabwe’s controversial ‘fast track’ land reform programme in 2000 reduced the AGRITEX budget (Government of Zimbabwe and FAO, 2011; Anseeuw et al., 2012) and compromised the effectiveness of its extension service (Hanyani-Mlambo, 2002; Saravanan, 2008; Gwaradzimba, 2011). This encouraged donors to experiment with outsourcing.

 

Outsourcing is a way of contracting private service providers (including private sector firms, NGOs and farmers' organisations) to deliver information and ser-vices characterised largely as public goods (Heemskerk et al., 2008). These service providers are often paid from both public and donor funds. In Zimbabwe, outsourced agricultural extension services are usually a component of projects that are fully funded by donors as most donors stopped channelling funds through the Treasury in 2002. The donors contract private service providers and monitor their performance (Anseeuw et al., 2012). This differs from the approach adopted in Mozambique where private service providers are contracted by the government, but does not imply a lack of collaboration with, or accountability to, the Zimbabwean Government.

 

Griffith and Figgis. (1997) describe the potential benefits of outsourcing relative to public service delivery. These include cost savings, improved effectiveness and development of the private sector. They also refer to potential challenges such as reduced accountability of government for the quality and quantity of contracted services, and collusive tendering practices. However, there is very little empirical information about the costs and benefits of outsourced extension services or about the impact of these services on small scale farmers in southern Africa (Heemskerk et al., 2008). This study aims to provide stakeholders with objective information about these aspects of an outsourced extension service in Zimbabwe.

 

Although there are several donor-funded projects operating in Zimbabwe that provide outsourced extension services, this study examined only one project funded by USAID. The study area was located within the Mutasa district in Zimbabwe’s Manicaland province where outsourced extension services are well established and private service providers are actively recruiting new farmer clients. The project is managed by a private US-based company, Fintrac Inc. Fintrac contracts several NGO’s and agribusiness firms to service different parts of its programme in the target area. The study area is serviced by Favco, a local company that processes fruit and vegetables. 

This research examines the impact of an outsourced extension service on small farmers and assesses the financial cost and benefit of this service in the study area. The study area was confined to the Honde  valley,  an  area  of  500 km2  located  in  the Mutasa district approximately 100 km north-east of Mutare, the fourth largest city in Zimbabwe (Mushunje, 2005). The valley is home to an estimated 1177 households located in five villages. Agriculture is the main economic activity. Annual rainfall averages 850-1000mm, but is restricted largely to the summer months from October to April. Crops grown include maize, bananas, coffee, tea, tubers and legumes (Mtisi, 2003; Mushunje, 2005). Topography ranges from steep to gently undulating slopes and most farmers irrigate crops using gravity irrigation. Approximately 600 of the smallholders farming in the study area use the agricultural extension service provided by Favco under contract to Fintrac (Fintrac, 2014). The service includes training and advice on farming practices (especially bananas and subsistence food crops), loans for seasonal farm inputs, and help accessing markets. 

 

Sampling method and data collection

 

The methods applied to the study’s two key objectives draw on the same primary data gathered from farm households using a rigorous sampling design. Two sample surveys were conducted between April and June 2014. The first was a representative sample of all households in the study area. A two-stage cluster sampling method was used to select these households. At the first stage of sampling, two of the five villages (primary stage units or PSUs) were selected with probability proportionate to an estimate of their size. These estimates were based on a physical count of households (secondary stage units or SSUs) in each village. Households in each of the selected villages were then listed and a simple random sample drawn from each list using a constant sampling fraction (20%). This approach produces a self-weighting sample that can be analysed as if it were a simple random sample. A total of 152 households were surveyed, representing almost 13% of the estimated 1177 households in the study area.

 

The second survey was a census survey of all ‘new’ clients serviced by Favco in the study site. New clients were defined as those smallholders who with the firm’s assistance, planted tissue culture banana seedlings in 2012 to harvest an improved banana crop between January and June 2014. A total of 32 new clients were surveyed. The samples together yielded 184 respondents. Of these, 94 were households that had been serviced by Favco (including the 32 ‘new’ clients) and 90 were non-clients, that is nc=94 and nnc=90. 

 

A uniform and structured questionnaire was administered in personal interviews with the de facto head of each sample household and with all ‘new’ project clients. The questionnaire gathered information on, inter alia: household and farm charac-teristics; farm enterprises, seasonal input purchases, and income from products sold in the 2013/2014 season; use of advisory, market and other services provided by Favco and the season in which each of these services were first used by the household. It was intended to solicit information from clients on their willingness to pay for Favco’s extension service but this question was removed in case it discouraged farmers from participating in the survey.

 

Measuring the impact of Favco’s extension service on farmers

 

To assess the impact of a project in the absence of randomisation, it is important to compare similar households within the client (treatment) and non-client (control) groups (Rosenbaum and Rubin, 1983; Mendola, 2007; Khandker et al., 2010). In this study, propensity score matching (PSM) was used to identify a subset of client and non-client households similar in respect of observed family and farm characteristics that were unlikely to vary in the short-term. These  variables included the  age  and  gender  of   the household   head;   land   and   labour   endowments      per     adult equivalent ; dependants per adult equivalent; per adult equivalent value of farm implements and tools owned before project intervention; and village location. A logit model was estimated to predict the probability (Pi) that the ith household would use the extension service. Clients were then paired with non-clients that had similar Pi using the PSM procedure available in SPSS version 22 (Field, 2009). 

 

Univariate t-tests for the equality of means across these comparable groups of clients and non-clients were made to reveal significant differences in variables measuring project outcomes. Where significant differences were detected, the ‘general treatment model’ (Khandker et al., 2010, p.25) was applied to control for the effects of observed and unobserved variables that affect outcomes but which are not related to the project. While the PSM accounted for observed characteristics that are unlikely to vary in the short-term, it excluded variables like prior investment in fencing and irrigation that could also influence participation. Following Khandker et al. (2010), the impact of an extension service on household outcomes can be measured by estimating the general treatment model:

 

 

Descriptive statistics for the household sample 

 

The descriptive statistics presented in this sub-section were computed from data gathered in the household sample survey (n_h=152). These statistics serve to introduce variables used in subsequent analyses and help to describe an average household in the study area.

 

Table 1 presents the mean value of variables mea-suring household demographics. Very few adults work off-farm. This reflects the relative importance of farming as a livelihood. The virtual absence of off-farm wage em-ployment is also evident in the high proportion of male-headed households (86%). This contrasts with results from other studies of smallholders in parts of Southern Africa where men become migrant workers in towns and cities (Fenwick and Lyne, 1999; Kassie et al., 2012). Although household heads are relatively young (46.6 years) and reasonably well educated (7.5 years of schooling), they have acquired substantial experience as farmers (13 years). Household composition is similar to that reported in other studies of Zimbabwean smallholders (Mushunje, 2005; ZimVac, 2013).

 

 

Table 2 summarises information about household farming operations including annual cash revenue from crop and livestock sales. These estimates are based largely on recall although many respondents were able to produce receipts and invoices to support their estimates of sales and expenditure. Bananas are by far the most important cash crop, accounting for 75% of farm cash earnings. Many authors view a shift from subsistence staples to high value cash crops (such as bananas) as essential for the improvement of rural livelihoods (Jayne et al., 2001; Davis, 2006; Fan, et al., 2013). Maize accounts for more land than any other crop but is grown largely for subsistence purposes (Kassie et al., 2012) and generates only 5% of farm cash earnings. Livestock do not make a significant   contribution   to   farm    earnings.

 

Table 3 presents the mean value of important household assets. Although livestock do not produce significant income, they account for the largest share of the estimated market value of these  assets.  In Southern  Africa, smallholders keep cattle largely as a store of wealth (Doran et al., 1979; Bote et al., 2014). Irrigation equipment also accounts for a large share of total asset value. The vast majority of households in the represen-tative sample operated their own gravitational irrigation systems.

 

 

The impact of Favco’s extension service on farmers

 

The logit model estimated as part of the PSM process described in the methods section was statistically significant at the 1% level of probability with a Nagelkerke R² of 0.25. Land, labour and dependants were statistically significant and positive determinants of participation in the outsourced extension service. Age was a statistically significant but negative determinant of participation. The PSM matched 76 pairs of clients and non-clients. Unmatched cases were excluded from the treatment and control groups. The results of univariate t-tests for the equality of means across these comparable groups of clients and non-clients are presented in Table 4. These results reveal marked differences in variables measuring project    outcomes.   The  t-statistics,  which  test  for differences in per adult equivalent group means (to con-trol for differences in household size and composition), highlight large and statistically significant differences in crop revenue, crop net revenue, banana revenue, expenditure on farming inputs and services, and levels of liquidity between client and comparable non-client households.

 

While the results presented in Table 4 are encouraging, they could be misleading as univariate tests do not account for observed and unobserved variables that affect outcomes but which are not related to the project. The ‘general treatment model’ described in the methods section was applied to each of the outcome variables listed in Table 4 with the exception of livestock revenue which did not differ between treatment and control groups. The logit model (Equation 2) estimated in the first stage of the two-stage process was statistically significant at the 1% level of probability, returned a Nagelkerke R² of 0.40 and correctly classified 78% of the 152 matched households into their known treatment and control groups. All of the explanatory variables, including the instrument, were statistically significant and positive determinants of treatment.

 

Table 5 presents the results of the  second  stage regressions for each of the five (significant) outcome variables. All of the regression models were statistically significant at the 1% level of probability. There was no evidence of severe multicollinearity as most of the explanatory variables, including the predicted level of the outsourced extension service (?), had Variance Inflation Factors (VIFs) close to unity.

 

 

Age and farming experience exhibited modest collinearity with VIFs of 1.5 and 1.8 respectively (Gujarati, 2004: 362). The impact of Favco’s extension service on households is measured by the regression coefficient estimated for ?. The results indicate that the extension service had a positive impact on household crop income, adding per adult equivalent amounts of US$210 to net crop revenue, US$282 to crop revenue and US$293 to household liquidity. Expenditure on crop inputs and services increased by US$75 (t-statistic = 1.45) per adult equivalent. This bodes well for local economic growth as the local growth multiplier associated with increased agricultural earnings in the district is expected to be in the order of 1.8 (Hendriks and Lyne, 2003). The cash gains generated by the extension services investigated in this study were driven largely by commercial production of bananas.

 

Only two of the household and farm characteristics that influenced participation (namely, the farmer’s experience and the household’s land endowment) also influenced the outcomes presented in Table 5. Access to land and the efficiency of the land rental market are clearly important issues in promoting farm incomes and local economic growth. Empirical evidence from southern Africa shows a strong positive relationship between productive use of farmland, the efficiency of the land rental market and measures of land tenure security (Lyne, 2006).

 

Additional benefits perceived by clients

 

Other benefits perceived by client household (n_c=94) were also considered. Table 6 presents the incidence of clients that attributed improvements in socio-economic indicators to the outsourced extension service. Clearly the vast majority of clients perceived improvements in household food security, quality of diet, health, access to support networks, ability to cope with social setbacks, savings and child education. In addition, more than 95% of clients perceived improvements in the quality of their produce (appearance, size and storability) and farm inputs, and in yields achieved for their main cash crops. On a Likert-type scale of 1 (poor) to 5 (highly satis-factory), these clients rated their overall satisfaction with Favco’s agricultural extension service as 4.4. Almost 60% of the clients claimed that they had spent more on labour since becoming clients, and the mean number of permanent jobs created per client was 2.5.

 

Financial cost and benefit of Favco’s extension service

 

 

 

where C is the cost to the donor of the support that Favco provided    in  the  study  area  from  September  2013  to August 2014. Fintrac estimated this cost as US$39,276 (M. Chirima, personal communication, December 09, 2014) . The net incremental financial benefit of Favco’s extension service in the study area is therefore conservatively estimated as US$11,588 ([US$434,707-US$383,843]-US$39,276), a return of almost 30% on investment for the 2013/14 crop year. The Global Forum for Rural Advisory Services (GFRAS, 2012) claims that annual returns of 40 to 60% are the norm for investments in agricultural extension. However, the studies supporting this claim do not relate specifically to outsourced extension services or to Southern Africa.

 

Considering that Favco had serviced only 40% of the households in the study area, with just one quarter of these clients added during the 2013/2014 season, it is conceivable that this financial benefit could be reaped for several years to come as the service is extended to more households. Moreover, the previous section suggests a host of additional benefits that should be taken into ac-count, such as improvements in food quality, better diets and family health, and pro-poor employment creation. 

 

Participation in Favco’s extension service was positively influenced by the household’s land and labour endow-ment, investment in irrigation and fencing, and posses-sion of a mobile telephone. It was estimated that the extension service added per adult equivalent amounts of US$282 and US$75 to crop revenue and expenditure on crop inputs and services respectively. The data also suggest that the outsourced extension service produced other socio-economic benefits like improved food quality and food security. However, there was no evidence of increased livestock revenue.

 

While these findings support the view that agricultural extension services play an important role in raising farm incomes and creating employment opportunities in poor rural areas, they also highlight the need for an efficient land rental market to alleviate farm size constraints, for rural health services to alleviate labour constraints, for telecommunication services, and for smallholder access to capital to finance improvements like irrigation and fencing. In turn, secure land tenure is required for an efficient land rental market and to strengthen incentives for investment in improvements. In the absence

 

of these fundamentals, even well-resourced extension services will be less effective and less pro-poor than they should be.

 

 

An analysis of the financial cost and benefit of the outsourced extension service in the study area suggests an annual net incremental benefit of US$11,587, repre-senting a 30% return on the investment made by donors to finance the service - even when the socio-economic benefits are disregarded. These results suggest that there is good reason for donors to continue funding effective extension services to small farmers in areas of high agricultural potential such as the Mutasa district.

The author(s) have not declared any conflict of interest.

The authors gratefully acknowledge financial support from the New Zealand AID Programme, Ministry of Foreign Affairs and Trade (MFAT); and from Fintrac Inc. and its partner, Favco, for information and access willingly provided. Opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsor or organisations mentioned.