Livestock Research for Rural Development 22 (2) 2010 Guide for preparation of papers LRRD News

Citation of this paper

Participatory analysis of the farming system and resources in Wundanyi location, Taita district, Kenya: A livestock perspective

P M Mwanyumba, A Mwang’ombe*, E Lenihan***, F Olubayo*, M S Badamana**, R G Wahome ** and J W Wakhungu**

Ministry of Livestock Development, Department of Veterinary Services, Private Bag - 00625, Nairobi, Kenya
* University of Nairobi, Department of Plant Science and Crop Protection, P.O. Box 29053-00625, Nairobi, Kenya
** University of Nairobi, Department of Animal Production, P.O. Box 29053-00625, Nairobi, Kenya
*** University of Cork, Ireland


This study was carried out using two of the methods of farming systems analysis namely Participatory Rural Appraisal (PRA) and Static Survey. The objective was to analyze the mixed farming system in this area in view of the changes in demands, constraints and opportunities that have affected the agricultural sector in Kenya since the Structural Adjustment Programs.


The results show that, indeed, the area was negatively affected by the liberalization, land pressures and concomitant economic stresses, but the decline in technology adoption and production is not so drastic as to be declared irreversible. This decline in adoption is manifested in low sales of livestock and products and it appears to have been caused mostly by factors external to the farm especially resulting from liberalization and privatization of animal health and production services.


It is thus recommended that intensified extension and service provision accompanied by a commercialization approach are urgently required to stop further decline.

Key words: extension and service provision; farming systems analysis; low sales; participatory rural appraisal; static survey; technology adoption


The mixed crop-livestock system is the most common form of keeping livestock in developing countries. It supplies over 90% and 50% of the world’s milk and meat respectively, providing livelihoods to more than 70% of the world’s resource poor livestock keepers. This proportion is likely to increase with rising populations in sub-Saharan Africa (Thornton and Herrero 2001).


In Kenya, the system is characterized by a variety of food and cash crops combined with livestock. Dairy cows invariably have a central place, but farmers also keep other livestock mostly sheep, goats and chicken. The system is largely self-sustaining, but unable to provide adequate employment or household livelihood. It is therefore supported by off-farm income and remittances from the extended family (Thorpe et al 2000).


Various constraints to small-holder dairy productivity in the Kenyan highlands have been identified. These include erratic payments, low prices and low sales, unreliable market outlets and limited access to animal health and breeding services. It is therefore, uncertain whether the mixed system has been appropriately optimized and if the productivity level could be raised. Increasing productivity will enhance farm incomes and nutrition, reduce poverty and also supply dairy products to the growing urban populations (Karanja 2003). There is need for greater understanding of the production system, its objectives, constraints and resource distribution to the various components in order to suggest appropriate options for improvement. This study collected data to enable farmers in Wundanyi location to evaluate resource availability and allocation, constraints, coping strategies and opportunities.


Materials and methods 

Description of the study area


Wundanyi location is one of the locations in Wundanyi division of Taita District in Coast Province. The division consists of some of the high potential parts of the District in Agro-ecological Zone 3 i.e. Semi-humid and some of the drier lower areas in Agro-ecological Zone 5 i.e. Semi-arid (Jaetzold and Schmidt 1983). It has an area of 682.1 sq. km. and an estimated population (projections for 2008) of 64,056 people (District Statistics Office 2007). The average temperature in the District is 23.9 C with the lowest, which will be in Wundanyi division, at 16.4 C. The average rainfall per annum in Wesu, the study area, is 1,400 mm (Ministry of Finance and Planning 2001).


Data collection


Data was collected using participatory rural appraisal (PRA) and a structured questionnaire survey in the nine villages of Wundanyi location that make up Wesu sub-location.  These were done by a multi-disciplinary team of University of Nairobi lecturers, students and local Government officers having three interests namely livestock, crops especially African indigenous vegetables (AIVs) and human nutrition especially relating to HIV/AIDS.  The PRA was done in three days and the survey took one week.


A total of 70 farmers participated in the PRA. These farmers attended in response to a general announcement (not specific personal invitations) of the meeting by the Location Administration, Agriculture and Livestock officers. The discussions were conducted with all the participants mixed except during the Gender activity calendar analysis when men and women were separated and then mixed again for corroboration. The languages used were mainly Kiswahili with a bit of local language (Kitaita) and the leading team recorded the information in English. Most of the participants also understood English and the discussions were lively and participatory. The PRA tools as described by Lelo et al (1995) were used and the issues discussed were availability of resources; constraints and opportunities among others.  


The survey was done by enumerators walking and visiting households at equitable distances from each other within the villages without prior knowledge of the owners, but whose owners were present at the time of the visit. The enumerators used personal interviews with structured questionnaires that captured the following parameters: farm activities and labour, land size, cattle pests and disease management practices, cattle management systems including feeding and grazing strategies, cattle herd structure, level of milk production, access to extension services, and farm and non-farm employment among others. Sixty-nine questionnaires were administered.


Data analysis


The PRA information was recorded on flip charts. Mapping, time and trend lines, seasonal and gender calendars, diagramming and ranking were used to elicit, record, analyze and agree on community spatial, time related, social and technical data.


The survey data was entered into excel program and analyzed using Statistical Package for Social Sciences (SPSS) for windows, version 16.0. Analysis was done for descriptive statistics (means, standard deviations, minimum and maximum values), frequencies and percentages. Test for differences among categorical variables was done using the Chi-squared test at 5% significance level. The results are summarized in tables.


Results and discussions 

Description of household heads


Table 1 below shows information on the heads of the households. 76.8% of the sampled farmers were male and 23.2% female. The youngest respondent was 22 years and the oldest 77, with a mean age of 51 years. 13% of the farmers were not educated at all, 59.4% were of primary school level, 26.1% have completed secondary school and very few have gone past secondary level.  88.4% of the respondents had no off-farm employment. Household surveys usually describe such characteristics because in the social context there must be hierarchy of decision making, responsibilities and in some cases even consumption and all these can be determined by gender, age and education/experience. However, according to Kitaly et al (2005) it is not always easy in traditional small scale livestock production systems to decide who is the owner of an animal, as ‘ownership’ is not a simple or indivisible concept, but a ‘bundle of rights’ and furthermore the people who look after an animal are not necessarily those who own it or control access to its benefits.


Most of the farmers were fairly old, with low to middle level education and permanently employed in their farms.  These figures point at a subsistence livelihood and since education adds skills and knowledge to the human capital and age brings wisdom and conservatism, they may also have a bearing on the level of technology adoption and general farm management such as fodder and breeding management. Garforth et al (2005) discussed how lack of knowledge can contribute to poverty and new knowledge can open new livelihood opportunities and the relationship between poverty, power and knowledge and that depending on the way it is generated and disseminated, knowledge can either improve or worsen the situation of resource –poor farmers.


The fulltime employment in farms could be due to old age (inability to partake of other opportunities), but as discussed later in section 3.6, in rural areas dairying is a major source of employment for both family and hired labour (Staal et al undated) and furthermore Staal et al (2003) also pointed out that off-farm employment is not always readily available to farm family members.

Table 1.  Characteristics of the household heads interviewed during the survey










Not Educated



Educated up to primary school level



Educated up to secondary school level



Educated up to post-secondary level



Have off-farm employment



Have no off-farm employment



Farm resources


Table 2 shows the farm characteristics and resource availability. Farms are generally small with the smallest in the sample being 0.2 acres and the largest 8 acres with a mean of 2.3 acres. The farm area devoted to food crops was 52.7% and that to forage crops 24.1%. The total livestock area (31.6%) including the sheds is less than that of food crops. This land pressure is similar to that found in other smallholder mixed farming systems in Kenya as seen in the characterizations done in Central and Western Provinces (Staal et al 1997; Waithaka et al 2002; Mburu et al 2005). In Kiambu, the mean household land ownership was 2.68 acres and the proportions allocated to crops and livestock are similar to here. In fact, this apparent disproportionate allocation is not bad nor unfair since the dairy cow is a more efficient land user than crops and also utilizes not only the grown fodder, but also purchased fodder and feed, fodder from outside the farm, weed mixtures, crop residues and fodder from or on public land. Thus, Upton (2000) recommends that, because of increasing land and population pressure, the intensity of agricultural production must be increased and livestock production offers the prospect of increasing intensity of land use.


Most (44 %) of the farm workers are men, 16-60 years, closely followed by women (41.6%) of the same age. Some old men and women over 60 years also assist in the farms equally.  Slightly more boys assisted in the farms than girls. Thus, there is near Gender equality in farm duty allocations, but women are further burdened by household chores, which continue after the men have gone for recreation and well after the evening meal. There was a significant difference, (c2(5) = 154.696, P < 0.001), among the age categories. Gender roles are the socially constructed expectations for men, women and children and they vary greatly among societies (Kitalyi et al 2005).


Streams are the main sources of water both for home consumption (58%) and for livestock (61.6%), followed by piped water and then rainfall for those with storage facilities. These water sources are not far from the homesteads and there is adequate water for domestic and livestock use. However, the lay of the land and the amounts of water do not allow for large-scale irrigation and the few people who do horticulture irrigation, use hosepipes or hand-fetching from streams passing near their farms. The water availability and utilization could be improved by rain water harvesting and storage, terracing of the farms and avoidance of cultivation too near the streams which causes them to dry. As seen below in the dual function of Napier grass, livestock especially dairy have an important role in integrated land management when they cause the adoption of agro-forestry with nutrient cycling fodder plants and nutrient flow onto farms through feed collected elsewhere and brought onto the farm (Kitalyi et al 2006) and also soil fertility with manure.


The fodder plants grown include Napier grass (Pennisetum purpureum, Calliandra (Calliandra calothyrsus), Desmodium (Desmodium spp), and Kikuyu grass (Pennisetum clandestinum). As in other smallholder areas in the country especially central Kenya (Mwendia 2007), Napier grass is the dominant fodder plant; it is grown in small plots and along contours and serves both as fodder and for prevention of soil erosion. The majority of farmers (69.9%) produce their own fodder, 22.6% buy and 7.6% get it at no cost from neighbours. Forage inadequacy is a problem especially during the dry season. Lack of adequate planting space has been found to be a critical limiting factor to increased fodder production on smallholder farms in Kenya and it has been recommended that farmers should be trained on increasing productivity, conservation, appropriate utilization, introduction of other suitable fodder crops, other methods of fodder production  and ration formulation (Lanyasunya et al 2006).


Crop residues and weed mixtures are used as supplementary or replacement feed. Maize residues are the most commonly used, though banana stems, beanstalks, cabbages, kales/collards and sweet potato vines are also used. All the respondents indicated that the source of crop residues was 100% own farms. Another coping strategy is road-side grazing and tethering. Grazing is used by few people as roadside space has little biomass and it would also take them away from other farm work. Tethering is used by a few farmers with one or two animals that can be taken along to their roadside farms away from their main compounds and fed with farm forage while the farmers do farm work. Traditional grazing is not possible as all the land is demarcated into individual farms or public institutions and there is no free trust land (Personal observation).


Animal Health technicians were available within less than 1 km to only 12% of farmers, while 24% indicated that technicians were more than 5 km away. 21% of farmers can get extension officers within 1-5 km while for 77%, the officers are more than 5 km away. Inseminators were 1-5 km away for 27% of the farmers and more than 5 km away for 71%. Thus service providers are few and far from reach and this is a real constraint to technology extension and implementation. This has become a big problem in the whole country after the Structural Adjustment Programs induced privatization of some Government Services (Oruko et al 2000).  Wambugu (2001), in a study in Kiambu District to establish the extent to which extension services affected farming practice, found that only 32% of the farmers were in contact with the Government extension service and the most important sources of technical information were dairy cooperatives and neighbours although other sources were cited. In another study on the delivery of veterinary services in Kenya, where three key parameters were evaluated (access, acceptability and affordability), access rather than affordability appeared to be the primary constraint (Heffernan and Misturelli undated).

Table 2.  Farm characteristics, resource availability and use in Wundanyi location

Land sizes and utilization / Area, acres


Proportion, %

Area devoted to homesteads and other farm structures



Area devoted to livestock sheds



Area for food crops



Area for forage crops



Total farm size



Gender and ages assisting in the farms


Proportion, %

Men 16-60 yrs



Women 16-60 yrs



Women >60 yrs



Men >60 yrs



Boys 0-15 yrs



Girls 0-15 yrs



Distribution and utilization of water resources, %




















Distribution and utilization of feed resources, %


Out free

Out bought

Forage feed












Crop residues




Livestock services availability, %

< 1 km

1-5 km

> 5 km

Animal health technician




Extension officer








Agro-vet shops




Livestock feeding systems


Table 3 shows the proportions of livestock in the different feeding systems. The systems have been classified based on the feeding and housing of all the animals. Intensive means fed within a house/stall such as a zero-grazing unit for cattle with cut-and-carry for fodder and confinement for chicken. Semi-intensive means both intensive and sometimes free range or tethered outside in the case of ruminants; and extensive means completely free range or grazed outside the farm for ruminants. This is a feeding system classification which can be said to be a classification according to intensity of production, but the three criteria which have been used by Sere and Steinfeld (1995) to classify World livestock production systems (into eleven classes) are  integration with crops, relation to land and agro-ecological zones.


The 69 sampled farmers had a total of 84 cattle, 49 chicken, 20 sheep, 3 dairy goats and 2 pigs among them. 76% of the cattle were intensively reared in zero-grazing units, 17% were semi-intensive and 7% were grazed outside the farm. 50% of the sheep were kept extensive, while 45% were semi-intensive and only 5% were intensive. 49% of the chicken were left to fend for themselves, 35% were supplemented with purchased feed and household left-overs and 16% were intensive, completely fed on purchased feed. 67% of the dairy goats were tethered and also fed with cut forage and 33% were stall fed. The few pigs were all kept intensively. The figures are a reflection of the farmers’ objectives and allocation of resources and importance to the various farm enterprises.


Farmers spent a greater proportion of their time looking after cattle than other farm activities including crops. This was also reflected in the PRA daily activity calendars with the farm activities starting at 5 AM and ending at 6 PM.  The high proportion of stall-fed cattle is similar to that seen in the Kenyan central highlands by Omore et al (1999) who also found out that the frequency of stall-feeding increases with decreasing land sizes.

Table 3.  Proportions (%) of livestock in the different production systems, classified based on the feeding and housing of the animals





















Dairy goats










Breeding and reproduction


Most farmers use bulls for cattle breeding with 65% using out-sourced males, only 2% mating within their own herds. Artificial insemination (A.I.) is used only by 33% of the farmers. This observation differs from that reported by the National Veterinary Research Centre (1996) that nearly all farmers in smallholder dairy cooperatives breed their cattle by A.I. and bulls are therefore not important in this production system. The A.I. charges by the local inseminators range from KSh900 – 1500 (US$11.25 – 18.75) per insemination for local and imported semen respectively while owners of breeding bulls charge KSh400 (US$ 5) per visit.


The comparative cost of A.I. over use of bulls is not high considering its advantages. It also compares well with other farm inputs. Therefore, it is certainly not expensive. Farmers, however, are reluctant to meet this cost despite having knowledge that use of bulls results in poorer quality cattle and less milk production as indicated in the table of constraint analysis (table 6). Possibly, this reluctance could be due to unfavourable comparison of the fees with the average daily earnings and other expenses; the long time before the investment realizes returns and the perceived poor performance of A.I. in terms of conception rates and actual progeny performance. The use of A.I. has dropped in the whole country since the privatization of the services in 1992 (Omiti and Muma 2000; Karanja 2003; Department of Veterinary Services 2007) and there is need to explore the causes and how to boost the uptake of the private services.


75% of farmers get breeding rams and bucks from outside for their sheep and the few dairy goats. As in other areas of the country, most of the chicken are indigenous, kept in the traditional way (Mugambi 2007) with no clear breeding objectives or plans.


Livestock produce


Table 4 shows the numbers of livestock sold per year. More farmers sold bulls and cows than other species although the mean number sold per person was just 1.2 and the maximum 4.  Larger numbers of chicken and chicken products were sold than other animals probably because of the small size and ease of selling chicken and their products. Overall, the figures show that sales were made to meet small frequent cash requirements and larger needs once in a while rather than for commerce.


Milk production per day was minimum 2 lts and maximum 25 lts with a mean of 7 lts. Sales ranged from zero to 19 lts with a mean of 5 lts and consumption from 0.5 to 6 with a mean of 2 lts. The price of milk was reported at KSh20 (US$0.25) per liter. Sales figures are lower than those reported in Kiambu District (Staal et al 1997) at 7.6 Lts per day although the price is much higher. The potential of getting more income from sales of milk exists if yields and prices could be improved. Yields could be improved through better breeding and management practices while prices may be improved through better marketing organization and strategies.


Upton (2000), observed that in traditional areas of extensive, rangeland-grazing and mixed farming, a market usually exists for live animals, prior to their movement to urban slaughter slabs, and for small quantities of fresh milk. A review by Staal et al (undated) suggested that polices in Kenya have historically targeted achievement of national development goals in food security, employment and income generation and that these policies have influenced dairy production and marketing and have resulted in phenomenal increase in the contribution of smallholder farmers to total national marketed milk production.

Table 4.  Proportions of respondents (N=53) that sold livestock of different species and types in the study period. Milk is not included as it is sold daily.

Species and types

Proportion of

Respondents,  %

Minimum number sold/respondent


number sold/respondent







Mature cows










Extra calves





Local chicken















Female sheep/goats















Livestock ranking


The farmers’ opinion was sought on the importance of each livestock species as sources of food and income. Table 5 shows the ranking of the livestock in order of their importance. The dairy cow is the most important livestock, being ranked top as a source of cash and second as a food source. Milk is the only product that reaches the external market. The chicken is the second for cash and first for food. Fish was ranked last for both food and cash and this is not surprising since there is little fish farming and no water body at all in the area.


Staal et al (1997) similarly found that dairying was an important income generating activity for a majority of households in Kiambu and probably the single most important farming activity in the District. In other mixed smallholder areas in the country, dairying is also cited as the most important source of income and cash flow, but is done together with other livestock, mostly chickens, sheep and goats (Omore et al 1999). Dairying provides higher returns than crops and rural wage labour and is a source of employment for both family and hired labour (Staal et al undated).


Cattle are the main livestock species in the smallholder agricultural sector and are even given higher priority in feeding because of their multiple uses (Chawatama et al 2005). However, the integration with smaller animals addresses the problem of unpredictability of food and income supply; it provides stability, surety and variety; and furthermore smaller animals are more prolific, have lower requirements, are less risky and are easier to sell and use as food than larger species (Kitalyi et al 2005).

Table 5.  Livestock ranking for all species in order of their importance as sources of food and income. The ranking was done by the community themselves during the PRA as part of their livelihood mapping

Species, products, purpose and markets






Cash stock

Food stock

Local market

External market








































Guinea pigs













Evaluation of livestock constraints


The problems in livestock production were listed and ranked using pair-wise matrix ranking method and these are reported in table 6. High cost of A.I. services was ranked the most important problem in livestock production. This is borne by the data that shows 67% of farmers using bulls compared to 33% A.I.   The perceived expensive A.I. services were considered to lead to cattle of poor potential and thus lower milk production and this was exacerbated by pests and diseases and dry season fodder unavailability.


Fodder unavailability did not rank high in the list as might have been expected and this is probably because the inadequacy occurs only during the dry season. Low milk production is listed and is considered as a constraint although it is in fact a result and the causes are indicated. This is probably as a result of the nature of the local languages which have little difference between causal constraints and resultant problems – if it is not adequate, it is a problem. This could be a problem in itself as it seems to absolve the causes of the result. Lack of cooling facilities was not considered to be a major problem showing either that there is a high turn-over of milk to the market or that the quantities don’t demand these facilities. 


Constraint analysis, coping strategies and opportunities


Table 6 shows the constraints to dairy production analyzed for causes, coping strategies and opportunities. The primary goal of any PRA exercise is to initiate an interactive process between the community and the PRA team so that a community action plan can be prepared (Lelo et al 1995). Such outputs and conclusions are the culmination of careful planning and conduct of the PRA (Devendra 2007). The listing of the constraints and causes demonstrates knowledge of their problems by the community. Coping strategies is what they do currently to attempt to solve the problems and opportunities are possible solutions to the problems.  These constraints and aspirations are found in different order in other similar parts of the country and the Policy challenges have been discussed by, among others, Omiti and Muma (2000) and Muriuki et al (2003).


The identification of the opportunities by the farmers themselves shows that these are services, institutions and technologies they know of or have experienced, and indeed they have. Upton (2000) summarized policy instruments for promoting the development of the dairy industry as falling under three main headings: prices, institutions and technology change. All of the instruments that the Government of Kenya has undertaken over the years have had a positive impact on the farming system of this community. For example, the Kenya National Dairy Development Project (1980 – 1995); Taita-Taveta Dairy Cooperative Society (collapsed in 2005); Taita Horticultural Produce Cooperative Society (all but collapsed); Agricultural Finance Corporation (moved out of the District in the late 1990s); the previous Government run Animal health, breeding and extension services; all put dairy and horticulture farming in the District on a strong and sound footing. The problem seems to be sustainability and continued implementation of technologies after completion of projects and institutional changes. This could be a result of the farmers’ slow acceptance of change and pining for the good old days or the strain on the research-extension-farmer linkage occasioned by liberalization and privatization of services.


Intensified extension and service provision accompanied by a commercialization and ‘go-get’ approach by the farmers themselves are therefore urgently required to stop further decline. It may not be far-fetched to relook at the public good versus private good classification of services as recommended by Pica-Ciamarra (2008) that Governments retain the freedom to step into free markets and directly supply private goods when alternative instruments prove ineffective.  

Table 6.  Dairy Production constraint analysis, coping strategies and opportunities



Coping strategies


High cost of Artificial Insemination Services

Don’t know

The local bull

Appeal for low price; improved bull camps; training on the importance of AI; improved production for high returns

Low milk production

Poor breeds; inadequate feed and minerals; pests and diseases; poor shelter.

Local-bulls; purchase fodder; purchase-mineral supplements.

Training; loans; increase fodder production; improved breeding.

Pests and diseases

Lack of sprays; poor management; poverty; lack of equipment; bringing animals from outside; roaming dogs.

Zero grazing; local equipment; merry go rounds; preventive medication for incoming stock.

Revive cattle dips and improve management. 

Work harder; go for loans; avail animals for vaccination.

Low milk prices

Use of middle men; piece meal payment; collapsed cooperative movement; adulteration of milk; unclean milk production.

Clean milk production; no strategy for the others.

Training; improved collection/transport; form milk associations; cooling facilities to minimize spoilage; quality assurance.

Fodder unavailability during the dry spell

Low rainfall; low fodder; no alternatives; poverty.

No strategies; grazing by the road-sides

Fodder preservation; education on fodder preservation; planting fodder crops

Lack of Storage facilities

No facilities; low production level.

Boiling; selling locally; selling on credit to middlemen.

Cooling facilities; cooperatives and external markets; value addition e.g. yoghurt, ghee.


This study has made the following conclusions:

 We therefore recommend capacity building of the farmers and availability of micro-credit facilities to enable the community move beyond the coping strategies towards exploitation of the opportunities and commercialization.



This study was supported by funds from the Irish Aid through the University of Cork, Ireland. The cooperation of the PRA team, the farmers and the enumerators is highly appreciated.



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Received 1 January 2010; Accepted 4 January 2010; Published 7 February 2010

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