Livestock Research for Rural Development 22 (3) 2010 Notes to Authors LRRD Newsletter

Citation of this paper

A dynamic study of smallholder mixed farms in Wundanyi location, Taita district, Kenya: activities, performance and interactions

P M Mwanyumba, R G Wahome *, A Mwang’ombe**, E Lenihan*** and M S Badamana*

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


This study undertook a dynamic survey of a sample of thirty mixed dairy-crop farmers for 11 months over two rainy seasons. The study identified the farmer’s objectives and observed the system over a sufficient period to get a quantitative and qualitative measure and trends of their activities, performance and interactions. The farming system was identified to be a low input, low output system although there are little exogenous shocks on both crops and livestock. There is, therefore, room for improvement in optimization of resources and productivity. This can be done by diversification and stabilization of production with other crops and livestock species coupled with Government social protection measures.

Key words: Diversification and stabilization; dynamic survey; low input, low output; optimization of resources; social protection


Smallholder crop-dairy cattle systems contribute, through milk consumption and income generation, to both food security and alleviation of poverty for the majority of smallholders in many areas of Kenya (Muriuki et al 2001). Although Jaetzold and Schmidt (1983) recognized the potential of Wundanyi division particularly for dairy and horticulture, the predominantly rain-fed smallholder farming practiced in this area is at risk of the prevailing climate change and Taita District is not self-sufficient in food. One of the coping strategies has been movement to the lowlands which have less agricultural potential, but are more expansive and more virgin and therefore perceived to require less inputs especially fertilizer. However, the lowlands are drier and therefore more likely to be degraded and cannot sustain increasing population pressure (Jahnke 1982).  Intensification was one of the principles that underpinned the Asian green revolution and that can be applied to Sub-Saharan Africa (Jones 2008).  The livestock revolution requires intensification and optimized nutrition, disease control, breeding, credit and other inputs and marketing services (Upton 2000). It is also necessary to ensure that intensification is in line with production objectives. However, there is inadequate understanding of the objectives of production in this farming system, the levels of production and the role and development potential of livestock. This study identified the farmer’s objectives and observed the system over a sufficient period to get a quantitative and qualitative measure and trends of their activities, performance and interactions.


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 study was undertaken in Wesu sub-location which is in the high agriculture potential zone, ‘Semi-humid, 3’ (Jaetzold and Schmidt 1983). The average rainfall in the study area is 1,400 mm per annum (Ministry of Finance and Planning 2001) in two rainy seasons – the short rains in October to December, and the long rains in March to July. 


Farms are generally small and the farming system is mixed rain-fed with the dairy cow being ranked the most important livestock as a source of cash and food. The main crops grown are maize, beans, bananas, cabbages, kales, tomatos, sweet potatos and some indigenous vegetables (black nightshade and amaranthus).


Data collection


The sampling frame was the nine villages that were involved in a participatory rapid appraisal (PRA) and baseline survey in a previous study. Due to logistical and cost considerations, the sample size selected was 30 farmers, the minimum recommended as ‘not being small’ (Petrie and Watson 1999). Purposive sampling was done to get farmers with a high degree of conscientiousness able to participate in the long study.


Data was collected using direct measurements and observations by six research assistants recording five farmers each, once every two weeks. The study was dynamic (that is, it evaluated the  farming system components  over time as discussed by Quijandria 1994) and longitudinal (it gathered information from the same set of respondents through repeated visits over a defined period as discussed by Staal et al 2003). It was undertaken for 11 months over the two rainy seasons. Quijandria (1994) divides the compilation of system characterization information into six stages, namely: - defining the boundaries of the system, determining the components, determining the social component, determining interactions, determining system inflows and determining system outflows. In this study, the following information was recorded:

Data analysis


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. Analysis of variance (ANOVA) at 5% significance level was used to test for differences in the continuous variables measured. The results are summarized in tables and charts


Results and discussion 

The farm components


Table 1 shows the land, household and livestock characteristics at the beginning and end of the study period.

Table 1.  Mean land, household and livestock herd sizes at beginning and end of the study period (N = 30).


Farm acreage


School children

Non-school children

Total cattle




April 2008









February 2009









Grand mean of all months









The human population in the sample was 218 at the beginning and 172 at the end. On average, the mean household composition was 7.8 people comprising of 3.4 adults, 2.3 schoolchildren and 2.1 non-school going children. The livestock population converted into Tropical Livestock Units, at conversion factors given by Jahnke (1982) (cattle 0.7, sheep and goats 0.1, chickens 0.01) was 73.8 TLU and 61.3 TLU at the beginning and end respectively. Among the monthly human and livestock population changes, only the changes in total cattle and chicken were significant (P<0.05) and these can be explained by sales or purchases and other herd dynamics including births and deaths.


Jahnke (1982) lists the resources for livestock production as livestock themselves and land, but clearly, if people, land (including forage and food crops) and livestock are interacting components of one system, then people also are livestock production resources as much as livestock are human development resources. The 2008 population density for Wundanyi division projected from the 1999 census is 91.3 persons per sq. km. (District statistics Office 2008). The land resource availability calculated with the figures at the beginning of the study is 0.47 acres per person and 1.40 acres per livestock unit. The human: livestock ratio is 0.34 livestock units per person and 2.95 people per livestock unit.  Livestock appear to be more adequately catered for than people, but these are only bare-resource availability indicators. The minimum and maximum farm acreages were 1 and 18 acres respectively with a mean of 3.43 acres. The average total livestock units per farm/household is therefore 2.47.


Table 2 shows the comparison of these parameters with figures for similar systems in SubSaharan Africa and the World calculated from Sere and Steinfeld (1995).

Table 2.  Comparison of resource parameters with other parts of Africa and the World in mixed rainfed humid and sub-humid tropics and sub-tropics systems.


Study area

SubSaharan Africa


Arable land per person, acres




Arable land per head of cattle, acres




Human : Livestock ratio




Livestock : Human ratio




Parameters for SSA and the World were calculated from figures for the human population and resource base in mixed rain fed humid and sub-humid tropics and sub-tropics systems (Sere and Steinfeld 1995).

Sere and Steinfeld (1995) observe that when comparing livestock resource availability indices among systems, within systems and across countries, a very wide range of resource endowment per inhabitant can be observed and developed countries tend to be substantially better endowed per inhabitant with land and livestock than developing countries. This study area is better endowed with arable land per person than the average of SSA, but not the world. It has much less land per head of livestock, but more livestock per person. As observed by the World Bank (2008) for developing countries in general, continuing demographic and land pressures can compromise survival if off-farm income opportunities are not available and farm production is not optimized.


Sansoucy (1995) and Dalibard (1995) exhaustively discussed the various ways that livestock drive food security and development and contribute to the environment. In this farming system, the interactions between the farm components were seen in the objectives, land space, food and fodder, crop residues, labour, income, soil conservation by Napier grass (Pennisetum purpureum) and manure for soil fertility. There is no animal traction or use of manure for building or fuel (neither dry nor biogas).


Farmers’ objectives for mixed farming


The farmers were asked an open question to give three reasons for this mixed farming. Ten different reasons were given which have been grouped into two broad objectives and four specific objectives. Table 3 shows the analysis of the objectives for frequencies and proportions of the respondents.

Table 3.  Comparison of farmer’s broad and specific objectives for mixed farming and the proportion answering to those objectives.

Broad objectives for farming

Specific objectives for mixed farming

Proportion of respondents  (N=84)

1. Do farm business

Farming as a business


2. Food security

Get family food


Get livestock feed


Soil  conservation


56% of the farmers gave food security as the broad objective and the rest 44 % cited business. Food security was further specified into family food (21.4%), livestock feed (6%) and soil conservation (28.6%). The reasons for the business objective were increasing income (51.4%), maximizing resources (16.2%), spreading risk (13.5%), self-employment (10.8%) and reduction of expenditure (8.1%). The reasons for food security which actually come out as the methods of attaining that objective are getting manure easily (44.7%), meeting own needs (21.3%), getting milk (17%), getting fodder for animals (10.6%) and improving soil fertility (6.4%). The objectives and reasons indicate that the farmers lean more towards subsistence than commercial farming. In comparison, Muriuki et al (2001) identified a commercial orientation in central Kenya. Unlike market-oriented commercial farmers, subsistence livestock producers follow broad production objectives that are driven more by their immediate subsistence needs rather than demands of a market and subsistence agriculture follows low-input and risk-averse strategies (Ayalew et al 2001). These classifications however, merely indicate “orientation” and not rigid conformation and Jaleta et al (2009) observed that there is no common standard for measuring the degree of household commercialization and that it depends on the sum of consumption and income effects of market shocks (risks) and the scale can easily be tilted by favorable policies and institutional arrangements.


Allocation of labour in farm activities


In an earlier participatory analysis of the farming system and resources study, the farm area devoted to food crops was found to be 52.7% and that to forage crops 24.1%. Figure 1 shows the changes in the farm area that actually occupied the farmers attention and labour at a time during the study period. 

Figure 1.  Seasonal trend of mean active farm area on crop and forage production
that actually occupied the farmers attention and labour at a time

This ranged from 0.36 to 0.53 acres that is, 10.5% to 15.5%, of the mean 3.43 total acreage. This area varied from month to month due to differences in short term activities probably dictated by the rainfall patterns. The differences in area among months were significant in ANOVA with a P< 0.05. Thus, farmers were active and working on at least 10.5% of their farms all the time and there was no time when they were idle. This continuous working, literally living off the land, appears to be a characteristic of smallholder subsistence farming. The other proportion of the farms not being worked on contained crops like bananas, maize or others already weeded or otherwise managed and left to grow.


As in other smallholder areas in Kenya (Omore et al 1999), farm labour resources consist of available family members and hired casual and /or permanent labour. The number of family members assisting in the farm ranged from a mean of 2.8 to 3.7 and varied (P>0.05) with time probably depending on the type of farm activities and the family dynamics. Figure 2 shows the allocation of labour in the farm activities.

Figure 2.  Differences in labour allocation to crop and cattle production activities during the study period (time in hours)

Hired labour was markedly higher than family labour for crops in April while family members availed more work hours on livestock than hired labour consistently throughout the study period except in November when they were equal. April and November are the peak months of the long and short rains respectively. Labour intensive activities such as weeding, crop spraying and harvesting take place during these periods necessitating additional labour.  More hours are put into cattle management than crops.  Jahnke (1982) also indicates that livestock production tends to be more complex and more demanding than crop production mainly because livestock has two ‘crops’, fodder and livestock, and they both require such routine daily activities as feeding, milking, egg collection, cleaning and fodder collection and carrying. These facts point out that technological interventions on livestock production are likely to assist the farming household more than those on crops.


More hired labour is allocated to crops than to livestock activities. This could be evidence of higher value attached to livestock and less trust in hired hands. This explanation is supported by the high ranking of dairy animals as indicated in an earlier PRA study. On the other hand, since livestock activities are more time demanding, they are likely to be more expensive to pay for and families would rather do them themselves either to save or because they can’t afford the wages. It is conceivable, therefore, that labour allocation can be used to gauge household wealth.


Inputs and outputs from the land


The main inputs into the farming system are human labour, plant seeds, manure, fertilizer, pesticides and acaricides, cattle forages and rain. The terrain of the land is hilly so most of the farm work must be done by hand. It is also difficult to irrigate the crops and fodder plots because the streams are small and lack adequate water. Farm activities follow the rainfall patterns. Land preparation takes place in January and February and from June to September. Planting and fertilizer/ manure application closely follows in February to May. Weeding is the main activity in the wet months while establishment of Napier grass is done just before the short rains. The main outputs are food crop harvests, cattle forages, milk, manure and some animal sales. Figure 3 shows the trends of outputs value less inputs costs in every two weeks of the months when the data was collected.

Figure 3.  Seasonal trend of output values less input costs (profits)
of food crops and livestock products in every two weeks of the months

This also agrees with the seasonal activity calendar. April – July are the main input months and the costs are high. November – January are the harvest months and the output values are high. The outputs are higher than the inputs throughout except in April. Therefore, there was profit made in the enterprises in all the months except April when there was a loss. February shows a marked decline indicating that there may be a drop to begin the cycle at a lower level. This could be because output is not continuous and stocks get depleted after some months of sales and consumption. Both the costs of inputs and value of outputs are low which means that the returns were not great. The variation in input costs between months was significant (P<0.001), while the variation in output values was not (P>0.05). This implies that the farmers worked most of the time, but made money only some of the time and this agrees with the subsistence orientation. This orientation is defined by the combination of food security as the overriding objective and the low level of purchased inputs and outputs so that any sales are made for meeting immediate needs rather than commerce (Jaleta et al 2009).


Livestock herd/flock structure


Table 4 shows the structure and composition of the total livestock herd/flock at the beginning and end of the study period.

Table 4.  Livestock structure and composition at the beginning and end of study period showing the sums, means, proportions and the change in the sum between the two ends.


April 2008

February 2009


sum change

Sum (TLU)

HH Mean

% cattle


Sum (TLU)

HH Mean

% cattle












Milk cows










Dry cows






























Total cattle

94 (65.8)




(56) 80





Pregnant cows











27 (2.7)




(2.5) 25






7 (0.7)




(0.4) 4






462 (4.62)




(2.36) 236















The mean numbers per family are small, with three or fewer cattle and even fewer sheep and goats. Cattle formed the bulk of the total Livestock Units with 89.1% and goats the least with 0.9%. The cattle herd structure has adequate stock for potential production and replacement (milk cows, pregnant cows, calves and heifers) and only few bulls. The composition is not very different from the ideal herd as recommended by Smith et al (1993), that is, 44.8% cows in milk, 9.2% dry cows, 7.6% pregnant heifers and 38.4% younger heifers and calves. There was a 17% decrease in the overall herd/flock size at the end of the study period and only the pregnant cows and bulls had increased by 62.5% and 200% respectively. As seen in section 3.1 above, only the changes in total cattle and chicken were significant (P<0.05) among the months.


The livestock composition compares to the average in other mixed smallholder areas in the country where 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). Chawatama et al (2005) also found that cattle were the main livestock species in the smallholder agricultural sector and were given higher priority in feeding and management because of their multiple uses. 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).


Total farm production and utilization of produce


Figure 4 shows the relationship and trends in the farm production, sales and consumption.

Figure 4.  Comparison of farm produce yields, consumption and sales in every
two weeks of the months from the beginning of the study period to the end

There was variation (P<0.05) in sales value among months, but not in yield and consumption (P>0.05). The relationship between farm production, sales and consumption shows that production and sales increased steadily from the beginning of the study period towards the end while consumption decreased slightly. The increase in sales matches the trend in profits seen in figure 5 3.


It appears that sales were done at the expense of consumption, which could indicate a conflict of objectives. On the other hand, it could mean creation of capital by individuals foregoing current consumption (Beardshaw et al 2001). This apparent contradiction confounded Mutsotso and Chirchir (2005) who commented that Taita farmers lead lower standards of living compared to the Embu despite earning more from horticulture than the Embu do from tea and coffee. The low level of farm production exemplifies that of all the Sub-Saharan Africa, which is the only region of the world where per capita food production has been declining for the past three decades, a situation which is not acceptable if the region is to meet the Millennium Development Goals (Jones 2008). Upton (2000), observed that although in smallholder subsistence systems some poultry, small ruminants and even cattle may be slaughtered for home consumption and small quantities of milk and eggs are used within the households, production of a marketable surplus necessitates the existence of an effective marketing system and policies for the development and diffusion of new technologies. In addition, as discussed by Sabates-Wheeler et al (2008), the Government can also assist with well-timed food- or cash-for-work e.g. public works projects as a safety net to address the seasonal under-employment and hunger that is typical of rain-fed agriculture systems


Comparison and trends of household incomes


Figure 5 shows the changes in family incomes during the study period.

Figure 5. Family incomes – farm, off-farm and outside remittances

Income from outside is larger than the others for most part of the year. Farm income was initially smaller, but became larger than employment income in August. All income from farm, off-farm and outside remittances was not steady. However, the trends show a steady increase in farm income from May to January and a decrease in off-farm employment income from August to February. This confirms that most employment is not permanent. The increase in farm income reflects the increase in sales seen in figure 4.


Off-farm employment is not always readily available to farm family members and this in fact renders the opportunity cost of family labour below the wage rate (Staal et al 2003).   In a study in Malawi (Takane 2007), it was concluded that off-farm income can help to reduce the risk of own-farm production, but it is also a source of income disparity and provides little opportunity for upward economic mobility to escape poverty. In this study, the income from outside remittances was taken to be that from family members employed outside the immediate circle of the farming system and not resident in the area. This income is probably out of the farmers’ control and depends on remitter factors.


As discussed by Sansoucy (1995) , Dalibard (1995) and Moll et al (2007), it is recognised that these measurements are for visible, recordable incomes, but there are other incomes in kind and intangible benefits which may make the farming system more productive and competitive than observed. These are such as weed control, use of manure, insurance value, household nutrition and cultural benefits.



This study makes the following conclusions:

It is therefore recommended that:



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



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Received 1 January 2010; Accepted 16 February 2010; Published 1 March 2010

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