High incidences of poverty and poor living standard has been characterized many Sub-Saharan Africa countries including Tanzania. Among other reasons, low agricultural productivity, and high population growths not matching with the available resources to support them are associated with high incidences of poverty in these countries (Kelly 1998; Irz et al 2001; Mwankemwa 2004; Mason and Lee 2005). Smallholder farmers predominates agricultural sector in Tanzania and other Sub- Saharan Africa countries. Smallholder farming has been characterized by low productivity. This situation is partly attributed to lack of capital and uses poor farming technologies by smallholder farmers, drought, and lack of market for the produce (Mwankemwa 2004). Lack of capital by smallholder farmers is linked to inaccessibility to formal credit institutions due to lack of collaterals by majority of smallholder farmers. In addressing the problem of lack of capital by smallholder farmers, the government of Tanzania has been encouraging farmers to start Serving and Credit Cooperative Society (SACCOS), encouraging financial institutions offering micro-credits to farmers under less tough conditions (i.e group landing schemes), as well as encouraging the Heifer in-Trust (HIT) credit schemes for the case of dairy cattle farming (Kashuliza et al 1998; Mohamed 2003; Mwakalile et al 2002; Mwankemwa 2004; Kyomo et al 2006; Ssendi and Anderson 2009). In HIT credit schemes some farmers are trained on dairy cattle management and thereafter are provided with one to two crossbred dairy cows (crossbred incalf heifer) for management under stall-feeding regime. Upon calving of the cows a farmer is suppose to pass on an equivalent animal from the offsprings to another farmer as a repayment for the credit. These HIT credit schemes have helped many poor farmers in the country who don’t have capital to buy dairy cattle to possess them. This type of scheme in the study area has been there over 20 years initially run by a project under Evangelical Lutheran Church of Tanzania (ELCT) and thereafter by Kagera Livestock Development Project (KALIDEP), in which both of the projects were donor funded. Donor support to the scheme ended up in 2001 and the scheme was left to be run by a trust i.e. Kagera Dairy Development Trust (KADADET) and farmers organizations (Kyomo et al 2006).
In general, as with other agricultural development projects in Tanzania the main aim of the HIT schemes was to improve welfare of smallholder farmers and hence poverty reduction. As in many other parts in Tanzania, limited information is available on the performance of these schemes in improving household welfare of smallholder dairy farmers in the study area. Previous studies in the area and Kagera region as whole concentrated on evaluating operational performance of the scheme as well as productive and reproductive performance of the animals. Similar apply to most other parts of Tanzania (Kyomo et al 2006; Asimwe and Kifaro 2007; Mwatawala and Kifaro 2007; Chenyambuga and Mseleko 2009). Based on this background, the main aim of this study was to evaluate the performance of the scheme in the study area in improving household welfare. Specific objectives of this study were threefold; 1) To compare annual profits from various enterprises including dairy cattle farming by smallholder dairy cattle farmers, 2) To determine the role of manure from dairy cattle farming in improving soil fertility and hence improved crop productivity, 3) To determine the role played by small scale dairy cattle farming in improving household income, possession durable assets and food security status. These information are important for policy makers and development agencies for making more informed decisions with regard to livestock and poverty reduction.
Karagwe district is one of the seven districts of Kagera region, Tanzania. The district is located on the North western corner of region. Annual rainfall in the district ranges from 800mmHg to 1000mmHg. Average annual temperature in the area is 26oC. Majority of households in the district rely on subsistence farming. Farmers in the area are engaged in both crop and livestock production. Major crops grown in the area include Banana, Beans, Maize and Coffee; and major livestock kept include local goat, chicken and cattle as well as dairy cattle. Dairy cattle in the District are mostly kept in Kayanga ward. Kayanga ward is located on north- eastern part of the district. About one-third of all small scale dairy farmers in the district are found in Kayanga ward and nearly a quarter of all rural households in the ward are involved dairy cattle farming. This was one of the major criteria for choosing Kayanga ward in the district as a study area.
Data collection involved interviewing randomly selected dairy farming households involved in small scale dairy farming for at least three years as well as non-dairy farming households as control. A total of 38 and 30 households were selected for dairy faming and non-dairy farming households, respectively. A face to face interview with household heads was carried out using a semi-structured questionnaire. With regard to households involved in dairy farming, a questionnaire was designed to capture information related to general characteristics of the household and the household head; farmland ownership and use pattern; production, inputs, costs and revenues/income from dairy farming and other non-diary farming activities; income from non-farm activities; expenditure of income from dairy farming; assets ownership; perceived benefits and constraints to dairy farming. On the other hand, a questionnaire for non-dairy faming households involved similar information with the exception to the information related to dairy farming. In-depth interviews with key-informants i.e. livestock extension officers were also carried out in order to have a further insight to issues pertaining to dairy cattle farming in the study area.
Data were analyzed for descriptive statistics (i.e. means, frequencies) using Statistical Package for Social Sciences (SPSS) computer program. The SPSS package was further used for performing Chi-square tests for ascertaining associations between categorical variables; t-test, One- Way Analysis of Variance (ANOVA I) and Duncan Multiple Range Test (DMRT) for comparing means.
Results and discussion
Characteristics of the respondents
General characteristics of the interviewed respondents (i.e household heads) are indicated in Table 1.
Table 1. General information of the respondents
Variable Dairy farmers (n=38) Non-dairy farmers (n=30) All (n = 68) -value
Male 68.4% 70.0% 69.1% 0.02NS
Female 31.6% 30.0% 30.9%
< 35 13.2% 13.3% 13.2% 0.20NS
35 -50 55.3% 60.0% 57.4%
51+ 31.6% 26.7% 29.4%
Married 92.1% 83.3% 88.2% 2.76NS
Single 0.0% 6.7% 2.9%
Widow 7.9% 10.0% 8.8%
No formal education 2.6% 0.0% 1.5% 2.21NS
Primary education 65.8% 80.0% 72.1%
Secondary education 18.4% 13.3% 16.2%
College and above 13.2% 6.7% 10.3%
NS = Non significant at (P> 0.05)
Results indicates distribution of household heads by sex, age, marital status and education level in the two types of households (i.e. Dairy farmers and Non-dairy farmers) were not significantly different (P> 0.05). Majority of respondents in both groups (i.e. more than 50%) were males, had age between 35 to 50 years, married and had primary education. These observations imply that there was no significant association between involvement in small scale dairy farming and these characteristics.
Means of livelihoods by the surveyed households
Results from Table 2 indicate that apart from small scale dairy farming, as with non- dairy farmers, all surveyed dairy farmers were also engaged in crop farming (i.e banana, beans, maize and coffee) with average land size for cropping per household being 2 acres.
Table 2. Sources of income by the surveyed households*
Activity Dairy farmers (n = 38) Non-Dairy farmers (n = 30)
Dairy 38 (100.0%) -
Crop farming 38 (100.0%) 30 (100%)
Small scale businesses (i.e. off-farm activities) 8 (21.0%) 10 (33.3%)
Other type of livestock 12 (31.6%) 14 (46.7%)
Data set was based on multiple response*
Small scale businesses were practiced by 20% and about one-third (33.3%) of the surveyed households for dairy farmers and non- dairy farmers, respectively. Substantial proportion of household in both groups (31.6% for dairy farmers and 46.7% for non- dairy farmers) were also involved in keeping other type of livestock specifically local chickens and to a lesser extent local goat and cattle. These results show a high degree of livelihood diversification by surveyed households in the study area. Livelihood diversification by most of rural households has also been reported elsewhere in Africa (Brown et al 2006; Degrande et al 2007; Roetter et al 2007) as a way of coping with uncertainties i.e crop production and market failures, a situation which is very common in these countries.
Information related to dairy farming
Dairy cattle farming in the area is practiced under zero grazing using mostly Friesian x Boran (F1) crosses. Forages for dairy animals are usually established pastures (Napier grass) from own plots and farm edges; natural pastures from communal lands, river banks and road sides; and crop residues i.e banana tops. Animal are also supplemented with concentrates such as maize bran, cotton seed cakes and minerals block (mineral lick). These concentrates are usually bought from local suppliers in the area. Majority of farmers (82%) owns between 2 to 3 dairy cattle of mixed age (Table 3).
Table 3. Distribution of households by number of dairy animals owned (mixed age)
No. of dairy cattle Frequency (n =38) Percent
1 5 13.2
2 17 44.7
3 14 36.8
More than 3 2 5.3
Since most farmers avoid keeping male calves/bulls due to economical reasons and hence tend to cull or sell them, therefore, most of dairy animals by these farmers are females. Source of bull for breeding by majority of farmers (more than 90%) is through Artificial insemination (AI) usually done by livestock field officers in the area.
Annual profit from dairy farming relative to other enterprises
Annual profit i.e. Total Annual Revenue less Total Annual Variable Costs for various enterprises by the surveyed dairy farming households in the study area is presented in Table 4.
Table 4. Annual profit for different enterprises by dairy farming households
Enterprise No. of household in the enterprise Annual profit per household per annum, Tsh. (Means ± S.D)
Dairy 38 1,000,150 ± 388,770a
Crop farming 38 1,032,432 ± 489,277a
Small scale businesses (i.e. off-farm activities) 8 950,000 ± 542,481a
Other type of livestock 12 131,833 ± 114,290b
a,bMeans with different superscript letters are significantly different (P<0.05)
S.D = Standard deviation
Results indicate that dairy farming, crop farming and small scale businesses had high profit compared to profit obtained from keeping other type of livestock. Results from the Table further shows that average profit per annum per household from each of the first three enterprises was around 1 million Tsh, and the differences between them were not significant (P< 0.05), indicating dairy farming contributes substantially to household income and it is as well equally important as with crop farming and small scale businesses as means of livelihood. This call for promotion of small scale dairy farming in the area to encourage more farmers to join it. Significant contribution of small scale dairy farming to household income has also been reported elsewhere (Muriuki et al 2001; Urassa and Raphael 2002), and hence its potential for poverty alleviation.
Food crop production by dairy farmers compared to non- dairy farmers
Decline in crop productivity as result declining soil fertility has been a major problem in Kagera region with Karagwe district not exceptional (Rugalema et al 1994; Baijukya 2004). Manure produced from cattle bans were found to be used by dairy farmers in crop farms and pasture plots for fertilizing the land for boosting production. Food crop production was compared between dairy farming and non-dairy farming households to determine whether the use of manures by dairy farmers has benefited them in improving productivity and hence food security. Results from Table 5 indicate that yield per acre for all major food crops in the area was significantly higher (P<0.01) for dairy farming households compared to non- dairy farming households.
Table 5. Agricultural productivity for food crops in the season 2007/2008
Variable Crop Dairy farmers
(Mean ± S.D) Non- dairy farmers
(Mean ± S.D) Difference t-value
Yield per acre Banana, bunches 314 ± 80 200 ± 40 114 7.25***
Beans, kg 231± 68 118 ± 32 113 8.30***
Maize, kg 152 ± 70 63 ± 11 89 3.08**
Land size per H/Hold, acres 2.30 ± 0.86 2.25 ± 0.87 0.05 0.25NS
H/Hold = Household ; S.D = Standard deviation
NS, ** , *** = Non-significant, significant at (P<0.01), and significant at (P<0.001), respectively
Increase in crop productivity as results of using manure from cattle bans was noted as one of the major motives for small scale farmers engaging in dairy farming (From a key informant, Kayanga ward). These findings are in agreement with studies by Utiger et al (2000) in Kenya Highlands, and Bayer and Kapunda (2006) in Southern Highlands of Tanzania. These observations demonstrate the role played by dairy cattle farming in improving crop productivity and hence improved household foods security and welfare.
Household income, value of assets owned and food security status by Dairy and Non- dairy farmers
Household income, current value of durable assets and food security status of a household are among of the measures of household welfare (Mwankemwa 2004). To assess whether dairy cattle farmers were better-off than non- dairy farmers and hence positive impact of the scheme these two groups were compared on these variables. Results from Table 6 reveal that dairy farming households had significantly higher average annual income (P< 0.001) and were relatively more better off in terms value of assets owned (P< 0.001) compared to their counterpart . This observation further indicates the role played by small scale dairy farming in improving household welfare.
Table 6. Average annual household income, value of house (s) owned and other durable assets in Tsh.
Variable Dairy farmers
(Mean ± S.D) Non- dairy farmers
(Mean ± S.D) Difference t-value
Household income per year 2,585,263 ± 648,210 1,398,333 ± 475,700 1,186,930 8.40***
Value of house (s) 3,457,895 ± 1,996,704 1,338,333 ± 1,083,339 2,119,562 5.68***
Value of other durable assets 1,368,812 ± 1,006,335 625,000 ± 291,359 743,812 3.98***
S.D = Standard deviation ; *** = Significant at (P<0.001)
In their study in southern highlands of Tanzania Bayer and Kapunda (2006) observed that Income from milk sales helped some smallholder families acquire additional land, improve their houses (and cattle sheds) ,finance small-scale businesses, send their children to secondary school, and expand the dairy business.
Animal protein intake (i.e. meat, eggs, milk) is a major problem in Developing countries including Tanzania and hence malnutrition in these countries is widespread (Mulangila et al 2002; Nielsen et al 2003). Low animal protein intake is partly been linked to its unavailability and being relatively expensive. In human, animal protein are source of precursors of body compounds e.g. enzymes, blood protein, antibodies, hormones and several amino acids required for protein synthesis (Lehninger 1982). In this study it was hypothesized that by keeping dairy cattle a household will access milk easily and hence consumes milk more frequently than non- dairy farming households, and further that additional income from small scale dairy farming will increase the ability of a household to buy other animal based protein rich food i.e. Fish, Meat (which in most cases are expensive) for home consumption and hence improved nutrition. In the current study a trend on number of meals per day in a household were observed to be similar in both groups, in which more than 90% of households in both groups consumed almost two meals per day. However, frequency of intake of some nutritious food stuffs (animal protein) differed significantly between the two groups. (Table, 7).
Table 7. Distribution of households by frequency of eating valuable foods in a month
Food type Category Frequency per month -value
< 5 times 5-10 times > 10 times
Meat/fish Dairy farmers 13.2% 63.2% 23.7% 8.36*
Non-dairy farmers 43.3% 46.7% 10.0%
Eggs Dairy farmers 63.2% 26.3% 10.5% 0.96NS
Non-dairy farmers 66.7% 23.3% 10.0%
Milk Dairy farmers 10.8% 10.8% 78.4% 24.15***
Non-dairy farmers 54.8% 25.8% 19.4%
NS, * , *** = Non-significant, significant at (P<0.05), and significant at (P<0.001), respectively
Dairy farming households tended to consume meat/fish and milk more frequently than non- dairy farming households and the differences were significant at (P< 0.05) and (P< 0.001), respectively. Majority of dairy farming households (78%) consumes milk more than ten times a month compared to only 19% for non- dairy farming households. A substantial proportion of non- dairy farming households (54.8%) consumes milk less than five times a month (Table 7). While more than 80% of dairy farming households indicated to consume meat/fish for at least five times a month, only 56.7% do so for non- dairy farming households. Results further show that almost a quarter of dairy farming households consume meat/fish more than ten times a month as opposed to only 10% for non- dairy farming households. This observation indicates that apart from household income and values of durable assets, dairy farming households were also better-off nutritionally compared to non- dairy farmers and hence relatively more food secure. These findings underscore the potential role played by small scale dairy farming in reducing malnutrition. Both groups tended to consume egg less frequently in which majority of households in both groups consumed it less than 5 times a month. Taboos against egg consumption by some rural households in Kagera region (Lupiya 2007) could partly be responsible for the observed trend.
Challenges to small scale dairy farming in the area
In the present study small scale dairy farmers were also asked to rank problems they perceive to constrain them in small scale dairy farming in their area. Results from Table 8 reveal that unreliable milk market was the most important problem followed by high prices of drugs and concentrates. Unreliable milk market and high prices of concentrates were also revealed in a study by Urassa and Raphael (2002) in Morogoro urban district, Tanzania.
Table 8. Constraints to small scale dairy farming in the area as perceived by small scale dairy farmers
Problem Rank Total frequency Total weighted scorea
1st 2nd 3rd 4th
Unreliable milk market 18 (72) 13 (39) 2 (4) 0 (0) 33 115
High prices of drugs 11 (44) 9 (27) 7 (14) 1 (1) 28 86
High prices of concentrates 5 (20) 5 (15) 12 (24) 0 (0) 22 59
Limited extension services 1 (4) 5 (15) 1 (2) 2 (2) 9 23
Lack of AI services2 1 (4) 1 (3) 4 (8) 5 (5) 11 20
Lack of breeding bulls 2 (8) 1 (3) 3 (6) 2 (2) 8 19
Figures in brackets are frequencies and those out of brackets are weighted scores1.
1Weighted scores were obtained by multiplying frequencies by a weight of a respective rank with a 1st , 2nd, 3rd and 4th ranks given a weight of 4, 3, 2 and 1, respectively.
aTotal weighted score for each problem was obtained by summing up individual weighted scores from different ranks (indicated in brackets) in a problem.
2AI = Artificial insemination.
Finding external market for excess milk produced in the area could reduce the problem of milk market. However, this would require encouraging private individuals or farmers groups to establish small scale milk processing plants for increasing shelf life of milk in which farmers could sell their milk there. In turn, processed milk from these plants could be sold to external markets. High prices of drugs and concentrates in the area have mainly been attributed to few veterinary shops in the area. Again encouraging private individuals to initiate more veterinary shop could help reducing prices of these inputs.
Conclusion and recommendations
• Small scale dairy farming contributes significantly to household welfare in a study area and hence need to be promoted. Unreliable milk market, high prices of drugs and concentrates are the major challenges to small scale dairy farming in the area. Encouraging private individuals and/or farmers groups to establish small scale milk processing plants as well as private individuals establishing more veterinary shops could help alleviate these problems.
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Received 2 December 2009; Accepted 5 January 2010; Published 7 February 2010
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