3 Adoption and impact surveys of smallholder dairying in coastal Kenya

Numerous dairy-related technologies and practices could be considered in a study of adoption. Previous studies have examined the use of 20 technologies and practices associated with smallholder dairying in six districts of Kenya (Metz et al 1995), but not the factors associated with their adoption. This study focuses on a smaller number of related adoption decisions faced by smallholder farmers in coastal Kenya. The ownership of grade or crossbred animals is a key element in the development of intensive dairy production. Grade and crossbred (G/C) dairy cows have higher potential for milk production when adequately fed, and yet are more susceptible to diseases (e.g. ECF and trypanosomosis) common in many areas of Coast Province (Maloo et al 1994). Grade and crossbred cows require more feed than local cows to produce milk up to their potential. Because seasonal feed shortages have been identified as constraining milk production, the development of improved feeding systems has been a focal point for previous research (Reynolds et al 1993).

Work started in early 1997 on planning the adoption and impact study. The objective of the study was to determine the factors that influence partial or complete adoption of dairy technology. The technology was defined as ownership of a crossbred or grade dairy animal, the planting of the forage Napier grass, and the use of the infection and treatment method of immunisation against ECF. Questions as to whether adopters of this technology later 'de-adopted' or substantially modified their practices after the initial adoption decision was made, were felt to be particularly important in Coast Province. In addition, the adoption survey was to deal with three complementary technologies: crossbred dairy cows, Napier grass and ECF vaccination. There are clear interdependences between the decisions to adopt the three technologies. This is complicated somewhat by the possibility of lags (and sequencing) of adoption. For example, in some cases the decision to adopt Napier grass may be conditional on the decision to adopt cows, but the decision to adopt cows may not be conditional on the decision to adopt Napier grass, if the forage was planted a number of years after the crossbred cows arrived. Alternatively, to the extent that a package of technologies was required by the NDDP, the interdependence of adoption decisions may be mostly due to programme requirements. A series of surveys was designed to address these and other issues.

Studies of the factors influencing adoption of agricultural technologies often focus on household resource endowments, characteristics of the household head, location of the household, the nature and extent of information provided before adoption, and the characteristics of the technology (Feder et al 1985). In coastal Kenya non-farm jobs and businesses are key alternatives to intensification of agriculture for farm households (Waaijenberg 1994), but may also provide income needed for investment in more intensive dairying. Accordingly, the surveys were required to collect information on location, characteristics of the household head and sources of information used by the household head to make decisions about the choice of agricultural technologies. The surveys also included information about the characteristics of the household, perceptions about the availability of the G/C animals, availability of seeds and planting materials for Napier grass, and access to ECF immunisation. Households were also asked about their perceptions of the accessibility of the inputs and services associated with the three technologies. This information was then to be used to develop econometric models of adoption and impact (Nicholson et al 1999).

The first task was to compile a complete inventory of all households with small- or medium-scale farmers with dairy cows for the project area. The project area (Figure 1) encompassed agro-ecologies CL3 and CL4 in Kilifi District (Bahari, Kaloleni and southern Malindi divisions) and Kwale District (Kubo, Matuga and Msambweni divisions). In 1998, the boundaries of Kilifi District were adjusted to accommodate a new district, Malindi, and Malindi Division of the old Kilifi District became part of this new district. The areas south and north of Mombasa afford a substantial contrast in conditions, notably differences in trypanosomosis challenge and infrastructural development. Ministry of Agriculture staff completed the inventory, essentially a census of dairy households, early in 1997. Three separate surveys of farm households were conducted during 1997 and 1998, based on the inventory of 750 households with dairy cows in the three districts.

Adoption Survey

For the 'Adoption Survey' in June and July 1997, 75 dairy adopters and 125 non-adopters were surveyed in the three districts. The adopters, defined as households owning at least one grade or crossbred (G/C) dairy animal, were randomly selected from the inventory of all adopting households. The sample of adopters was stratified by division, the administrative unit below the district level. The total number of farmers interviewed from each division was proportional to the number of households in that location (Table 1). Non-adopting households were selected randomly from lists of 20 neighbours of adopting households.

Table 1. Households, adopters and number of survey respondents by division.

1. Source: CBS (1994).

Impact Survey

The 'Impact Survey' administered during February to April 1998 followed the same sampling procedure; some 200 households not contacted during the adoption survey were interviewed. Indicators of nutritional status for pre-school children were collected for 112 children in these households.

Detailed Survey

The 'Detailed Survey of Dairy Adoption History' consisted of semi-structured interviews with 29 farm households randomly selected from the households participating in the impact survey. Of the 29 households, 15 had previous experience with G/C dairy cattle and 14 had no experience with more intensive dairying.