the moremilkit project: baseline...

THE MOREMILKIT PROJECT:

BASELINE REPORT

2014

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TABLE OF CONTENTS

TABLE OF FIGURES ..................................................................................................................................... 4

TABLE OF TABLES ....................................................................................................................................... 4

ACRONYMS AND ABBREVIATIONS ........................................................................................................ 6

INTRODUCTION .......................................................................................................................................... 7

EXECUTIVE SUMMARY ............................................................................................................................... 9

CHAPTER 1: SAMPLING METHODOLOGY .......................................................................................... 12

1.1 Study sites ........................................................................................................................ 12

1.2 Sampling of households ................................................................................................... 13

1.3 Data collection, storage and analysis .............................................................................. 14

CHAPTER 2: HOUSEHOLD CHARACTERISTICS .................................................................................... 15

2.1 Household size ................................................................................................................. 15

2.2 Household head principal activities ................................................................................. 16

2.3 Household membership to groups .................................................................................... 16

CHAPTER 3: ASSET OWNERSHIP AND MANAGEMENT ................................................................... 18

3.1 Land ................................................................................................................................. 18

3.2 Non-land asset ................................................................................................................. 19

3.3 Livestock asset .................................................................................................................. 20

CHAPTER 4: MILK PRODUCTION, UTILIZATION AND COST OF PRODUCTION .......................... 23

4.1 Daily milk production ....................................................................................................... 23

4.2 Household milk utilization ................................................................................................. 23

4.3 Annual milk sale patterns ................................................................................................. 24

4.4 Milk marketing ................................................................................................................. 25

4.4.1 Marketing of fermented milk ........................................................................................ 27

CHAPTER 5: BREEDING, ANIMAL HEALTH AND OTHER DAIRY RELATED SERVICES .................... 28

5.1 Breeding .......................................................................................................................... 28

5.1.1 Availability and use of insemination services ................................................................ 28

5.1.2 Cost of insemination ...................................................................................................... 28

5.2 Animal health service ....................................................................................................... 29

5.2.1 Availability of animal health service ............................................................................ 29

5.2.2 Commonly treated diseases .......................................................................................... 30

5.2.3 Administration of animal health services ....................................................................... 30

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5.2.4 Decision making on administration of animal health services and selection of the service

providers ............................................................................................................................... 31

5.2.5 Cost of animal health services ...................................................................................... 32

5.3 Other services available in the study sites ....................................................................... 33

CHAPTER 6: CATTLE FEEDING SYSTEMS AND ALLOCATION OF LABOR TO DAIRY ACTIVITIES

..................................................................................................................................................................... 34

5.1 Feeding system and source of livestock fodder/feed ..................................................... 34

5.2 Source of livestock fodder/feed ..................................................................................... 35

5.3 Source of planting materials ............................................................................................ 35

5.4 Information regarding fodder ......................................................................................... 37

5.5 Labour and time allocation to dairy activities ................................................................. 38

CHAPTER 7: ROLE OF LIVESTOCK IN HOUSEHOLD FOOD SECURITY .......................................... 41

7.1 Household Dietary Diversity Score .................................................................................. 41

7.2 Food Consumption Score .................................................................................................. 41

7.2.1 Contribution of meat, fish and milk to the food consumption score ............................... 42

CHAPTER 8: INCOME AND MANAGEMENT OF HOUSEHOLD INCOME ....................................... 43

8.1 Income from sale of milk .................................................................................................. 43

8.1.2 Management of household income from milk ................................................................ 43

8.2 Income from sale of cattle ............................................................................................... 44

8.3 Income from other source................................................................................................. 45

CHAPTER 9: CATTLE MORTALITY ........................................................................................................... 46

9.1 Cattle mortality rates ...................................................................................................... 46

CONCLUSION ........................................................................................................................................... 49

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TABLE OF FIGURES

Figure 1: Map of Tanga and Morogoro regions. ............................................................................... 13

Figure 2: The annual pattern in milk sale. ............................................................................................ 25

Figure 3: Milk market outlet and price per liter of milk. ................................................................... 26

Figure 4: Milk marketing channels. ........................................................................................................ 27

Figure 5: Percent of households using different cattle feeding system for the local and exotic

breeds. ........................................................................................................................................................ 34

Figure 6: Source of planting material................................................................................................... 35

Figure 7: Percent of farmers citing reasons for sale of cattle .......................................................... 45

TABLE OF TABLES

Table 1: Study sites in Morogoro and Tanga regions ....................................................................... 12

Table 2: Distribution of sample size by district ................................................................................... 13

Table 3: Age (years) and % heads with education in female headed households ..................... 15

Table 4: Age (years) and % heads with education in male headed households ......................... 15

Table 5: Average number of men, woman and children per household ........................................ 16

Table 6: Primary household activities by gender (count) .................................................................. 16

Table 7: Percent households belonging to a group ........................................................................... 17

Table 8: Percent of households on land tenure system ...................................................................... 18

Table 9: Average land size (acres) owned ......................................................................................... 19

Table 10: Percent owning land by gender ......................................................................................... 19

Table 11: Percent of households under type of housing ................................................................... 20

Table 12:Non-land asset index disaggregated by gender ............................................................ 20

Table 13: Average numbers of livestock per household owned by men, women and jointly in

livestock keeping households .................................................................................................................. 21

Table 14: Percent of farmers raising cattle and (sum of cattle kept) by breed .......................... 21

Table 15: Percent of cattle kept (by animal type) ............................................................................ 22

Table 16: Number of cattle kept (TLU) per household ...................................................................... 22

Table 17: Average number of cows kept by breed .......................................................................... 22

Table 18: Milk production per cow (by breed) per day per lactation .......................................... 23

Table 19: Household utilization of daily milk production (in liters) ................................................. 23

Table 20: Forms of milk consumption at household level (count of households) ............................ 24

Table 21: Percent of households reporting availability and use of breeding services .............. 28

Table 22: Cost of insemination service (USD) ...................................................................................... 29

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Table 23: Count of dairy households using various sources of insemination/service providers 29

Table 24: Percent of household reporting availability and (use) of animal health services ...... 30

Table 25: Count of diseases commonly treated against in the previous 12 months .................... 30

Table 26: Percent of dairy farmers reporting having used animal health services and type of

service providers used in the previous 12 months .............................................................................. 31

Table 27: Percent gender involvement in decision making on administration of animal health

service in previous year ........................................................................................................................... 32

Table 28: Annual cost of animal health services (USD) ..................................................................... 33

Table 29: Percent of farmers reporting availability of extension and other services by district

..................................................................................................................................................................... 33

Table 30: Percent land under fodder and count of farmers growing fodder .............................. 35

Table 31: Percent of dairy farmers purchasing fodder and number of months purchased ....... 36

Table 32: Percent of dairy farmers feeding crop residues crop residue by source ................... 36

Table 33: Percent of dairy farmers using feed and mineral supplements .................................... 37

Table 34: Annual cost of planting material feed/fodder ................................................................ 37

Table 35: Source of information on fodder (count of farmers) ....................................................... 37

Table 36: Average labor time (hours) allocated to dairy activities by gender over one week

period ......................................................................................................................................................... 39

Table 37: Average household HDDS by district ................................................................................. 41

Table 38: Percent of households within food consumption group (FCS) ......................................... 42

Table 39: Percent contribution of meat, fish and milk to the food consumption score ................ 42

Table 40: Annual income from sale of fresh milk (USD) .................................................................... 43

Table 41: Annual income from sale of fermented milk (USD) .......................................................... 43

Table 42: Management of milk income (% by gender) .................................................................... 44

Table 43: Annual income from sale of cattle (USD) ........................................................................... 44

Table 44: Annual income from other sources (USD) ........................................................................... 45

Table 45: Count of households reporting death and (heads of cattle succumbing to associated

causes) ........................................................................................................................................................ 46

Table 46: Count of households reporting diseases attributable to cattle mortality .................... 46

Table 47: Cattle mortality rates over one year period .................................................................... 47

Table 48: Cattle mortality rates over one year period due to disease ........................................ 47

Table 49: Calf mortality rate by genotype ........................................................................................ 48

Table 50: Calf mortality rate due to disease ..................................................................................... 48

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ACRONYMS AND ABBREVIATIONS

AI: Artificial insemination

CAHSP: Community animal health service provider

FCS: Food consumption score

FGD: Focus group discussion

HDDS: Household dietary diversity score

MoreMilkiT: More milk in Tanzania and India

NAR: Average number of animals at risk

NGO: Non-governmental organization

PRA: Participatory rural appraisal

R-to-R: Rural production to rural consumption

R-to-U: Rural production to urban consumption

TLU: Tropical livestock units

USD: US dollars

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INTRODUCTION

Tanzania is rich in land and livestock resources relative to other East African countries. It covers

an area of 94.5 million hectares of which 94% is land and the remainder is water. Sixty

million hectares are rangelands with a carrying capacity of up to 20 million livestock units and

provide over 90% of the feed resource to the livestock. Livestock is one of the leading

agricultural sub-sectors in Tanzania. The livestock population comprises 21.3 million heads of

cattle, 43 million chicken, 15.2 million goats, 6.4 million sheep and1.6 million pigs.

A part from its direct role in household nutrition, livestock plays a major role in generation of

revenue and creation of employment. Its contribution to the growth of Tanzania’s Gross

Domestic Product (GDP) is an estimated 30% according to the Economic Survey report of

20101.

The cattle population puts Tanzania in third place in Africa after Ethiopia and Sudan.

However, cattle productivity with respect to milk production remains low because non-dairy

breeds such as the indigenous short horn East Africa zebu constitute the largest share of the

cattle population (96%). Milk produced by these herds is mainly for household consumption

with the surplus, if any, going to nearby market outlets (mainly neighbors).

Cattle productivity is also constrained by the seasonal variation in rainfall. Shortage of

adequate and good quality fodder is often experienced during the dry season, affecting

productivity and reproductive performance. Unfortunately, improving milk yields using

commercial feeds and feed supplements is difficult because they are reported to be

expensive and not easily accessible in the remote villages with poor infrastructure2.

Irish Aid, through a grant to ILRI and partners, is funding the More Milk in Tanzania

(MoreMilkiT) project that seeks to improve rural-based livelihoods through dairy production.

The project will address the following issues key to resource-poor cattle keepers: direct sales

of small volumes of milk by producers that preclude economies of scale, credit facilities to

improve access to basic inputs and services, appropriate organizational models for pre-

1 Njombe A.P., Msanga Y., Mbwambo N., and Makembe N. The Tanzania dairy industry: Status, opportunities

and prospects. Paper Presented to the 7th African Dairy Conference and Exhibition held at MovenPick Palm Hotel, Dar es Salaam, 25 – 27 May 2011. 2 Shayo M (ND): Potential of Mulberry as Feed for Ruminants in Central Tanzania. FAO Electronic Conference on

Mulberry for animal production (Morus1-L)

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commercial farmers for collective bulking of milk, entry into milk markets and access to inputs

and services, rainfall seasonality that causes seasonal availability of livestock feed and water,

hence unstable dairy incomes.

This report uses the baseline data collected at the end 2012 to provide more insights into

dairy production and challenges facing the smallholder dairy farmer in Tanzania. The first

chapter addresses the sampling strategy, while the following chapters discuss the results.

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EXECUTIVE SUMMARY

Nine hundred and thirty two households (932) were interviewed in the survey. Of these

households, 74% were cattle keeping, while the remaining 26% were non-cattle keeping

households. Over 96% of the households interviewed owned land with a further 5% renting–

in. The land acreage owned ranged between 3-14 acres while the average land rented-in

was 3 acres. In any given household, land was either owned by men or jointly by both men

and women. Regarding ownership and control of non-land assets, domestic and transport

assets were either owned by men or jointly owned, while farm assets were jointly controlled.

Men had the highest number of both local and improved cattle (19 and 3 heads of cattle,

respectively) as well as local goats, sheep, rabbits and donkeys. Exotic goats, poultry, pigs

and ducks were jointly owned.

The quality of housing in most households in Handeni and Kilosa (>50%) was below average,

while over half of the households in Lushoto and Mvomero had average to good housing

conditions.

A majority of the cattle rearing households (>80%) in Mvomero, Handeni and Kilosa districts

raised local breeds, while majority of households in Lushoto district (89%) kept improved

breeds. The number of cattle kept per household ranged between 2 and 46 total livestock

units (TLU) in Lushoto and Kilosa, respectively. Regarding type of animals kept, cows had the

highest share, taking up to more than 30% of the total herd. On average, households kept two

improved cows in Mvomero and Kilosa. On the other hand, the average number of local cows

was 16 per household in Kilosa.

Average daily milk production from improved cows was 5 liters/cow per lactation, while that

from local cows was 1.5 liters/cow. Most of the milk produced in Lushoto was sold either in

fresh or fermented form, while in Mvomero, Handeni and Kilosa districts, most of the milk was

consumed in the household. With respect to milk marketing, local restaurants offered the best

price per liter of milk (USD 54 cents) in Lushoto and Handeni. Individual milk buyers offered

the highest price per liter of milk (USD 48 cents) in Kilosa. Individual consumers/neighbors

absorbed the largest share of fresh milk followed by private milk traders. Fermented milk was

prepared by 34% (n=471) of the households with a lactating cow. Most of the households

processing the milk (96%) consumed it themselves. Only households in Handeni and Kilosa

reported selling the processed milk. The average price varied between USD 24 and 54 cents

per liter.

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Over 80% of the household interviewed reported availability and use of bull insemination.

Artificial insemination was not a popular method of breeding in the four study districts with

less than 10% and 2% of the respondents reporting its availability and use, respectively. The

cost of insemination was highest in Lushoto for both artificial (USD 18.3) and bull service (USD

2.7). Farmers used own bulls (339 households) or sourced for bulls from other farms (296

households) for insemination. A few of those using AI sourced mainly from private service

providers.

Availability of deworming and tick control services was reported by over 80% of the

households interviewed. Availability of vaccination services was reported by less than half of

the households interviewed (49%), while treatment was reported by slightly over a half of the

respondents. Tick-borne diseases and other vector-borne diseases were the main diseases

treated as reported by 39% and 21% of respondents, respectively. In Kilosa district, all

animal health services under study were administered by the farmer mainly with ‘professional’

advice. Similarly, deworming was mainly administered by the farmer with or without

‘professional’ advice in the other three districts. Vaccination was carried out by the

government health service provider in Lushoto, Handeni and Mvomero. Treatment of diseases

was self-administered in Mvomero and Handeni, while in Lushoto, treatment was done by the

government health personnel. In most households in the four districts (>50%), men made the

decision on use and the service providers to be contacted for any of the services. The pooled

annual cost of the services varied between USD 10 and USD 348. Other services available

included cattle extension services as reported by more than 40% of households in Mvomero

and Lushoto.

Grazing was the main feeding system in all the sites, except in Lushoto. Improved breeds in all

the sites were mainly stall fed except in Kilosa where all cattle were grazed regardless of the

breed. Lushoto district had the highest proportion of farmers growing fodder (83%) and the

highest proportion of land under fodder (22%). Over 50% of cattle keepers utilized crop

residues as livestock feed and most of these households (>90%) obtained it from own farms

while 15% purchased from other farmers. Lushoto had the highest proportion of farmers

(64%) supplementing dairy cattle with commercial feeds and bran was the main feed

supplement used by most of the dairy farmers (73%). The highest cost of feed was incurred in

the purchase of feed concentrate (USD158) followed by fodder (USD133).

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Labor for cattle-related activities was mainly provided by household men or women. Grazing

took the greatest share of time especially in Handeni, Mvomero and Kilosa where grazing was

the main feeding system for local cattle, the predominant breed in these sites.

Food security is an area of interest in smallholder dairy farming households. Food consumed

previously by the household was determined and used to measure household dietary diversity

(HHDS). Out of the 12 food groups considered in HDDS, all households had access to an

average of six different food groups in the previous 24 hours. The range was between 5 food

groups in Kilosa and 6 in the remaining three districts. On FCS, over 90% of the households

were within the acceptable food group consumption threshold.

The average annual income from sale of milk was high in Kilosa (USD 497). Dairy households

in Lushoto reported the lowest income from sale of milk (USD 89). Fermented milk was sold in

Handeni and Kilosa with an average annual sale of USD 6 and USD 16, respectively. The

income gained from the sale of morning milk was managed by the household female. The

average annual revenue from cattle sales was highest in Mvomero (USD1380) followed by

Kilosa (USD 974). Lushoto reported the lowest income from sale of cattle (USD103). Income

from other sources was USD 1013. Households in Mvomero had the highest mean revenue

from sources other than dairy (USD 1353) followed by Kilosa (USD 1049).

Cattle mortality was a great threat to smallholder dairy production. Overall, cattle mortality

rate was 7.8%. At district level, Mvomero had the highest cattle mortality (12.3%), mainly due

to drought, while Lushoto reported the lowest (2.6%). Exotic cattle registered a lower

mortality rate (2.3%) compared to the local breed (5.1%). Households with exotic herds had

lower calf mortality rate (1.4%) as compared to the local herds (6.0%) overall. A majority of

the cattle deaths were mainly associated with diseases (996) and drought (922). Diseases

implicated in most deaths were, among others, tick-borne diseases (reported by 39% of dairy

households) especially in Mvomero, Handeni and Kilosa.

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CHAPTER 1: SAMPLING, SAMPLE SIZE AND DATA

1.1 Study sites

The baseline survey was conducted in Morogoro and Tanga Regions of Tanzania. The specific

study sites (districts) were selected to represent a spectrum of cattle production and market

systems, the aim being to explore the potential to extend commercial dairying to marginalised

areas. The sites range from extensive/pre-commercial rural producers who predominantly own

zebu cattle and sell milk to rural consumers (R-to-R), to relatively more intensive/more

commercial rural producers who have relatively more improved dairy genes in their herds and

predominantly sell milk to urban consumers (R-to-U), usually via bulk traders (Table 1). These

strata also represent a gradient of increasing intensification. Using replicate regions

(Morogoro and Tanga), two districts were selected in each region, one R-to-R and the other R-

to-U.

Table 1: Study sites in Morogoro and Tanga regions

Region District

Market access

classification

Dominant production system

Morogoro

Kilosa R-to-R Extensive/Agro-pastoral (zebu)

Mvomero R-to-U Extensive/Agro-pastoral (zebu) with significant

semi-intensive & intensive (improved)

Tanga

Handeni R-to-R Extensive/Agro-pastoral & Extensive/Sedentary

(all zebu)

Lushoto R-to-U Extensive/Sedentary (zebu) with significant semi-

intensive & intensive (improved)

Key: R-to-R: Rural production to rural consumption (pre-commercial);

R-to-U: Rural production to urban consumption (more commercial)

Figure 1 shows the administrative location of Morogoro and Tanga regions. Morogoro is in the

coastal zone, while Tanga region is in northern zones of Tanzania.

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Figure 1: Map of Tanga and Morogoro regions.

1.2 Sampling of households

This survey followed a stratified random sampling method in which cattle ownership and

households were stratified and randomly sampled within each village. The sample size in each

village was proportional to the number of cattle keepers in each district. A total of nine

hundred and thirty two households (932) were surveyed. Of these households, 694 were cattle

keeping while the remaining 238 were non-cattle keeping (Table 2).

Table 2: Distribution of sample size by district

District

No. of cattle keeping households

No. of non-cattle keeping households

Total sampled households

Lushoto 165 56 221

Mvomero 178 60 238

Handeni 245 86 331

Kilosa 106 36 142

Total 694 238 932

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1.3 Data collection and analysis

Data was collected at household level using a structured questionnaire. The questionnaire was

organized by themes on dairy production. It captured household member profile, household

assets, herd structures and dynamics, breeding, animal health, milk production, feeding

systems, dairy-related activities and labour allocation, household nutrition and income. Data

was collected using the Census and Survey Processing System (CSPro) and later exported to

Stata13 where it was further cleaned and analysed. Descriptive statistics were generated and

used to write up the report.

Epidemiological calculations were applied in computing cattle mortality rates according to

Martin and others (1987), as follows:

Mortality rate = Number of dead animals over period (12 months)

Average number of animals at risk (NAR)

Where NAR = (Initial herd size + Current herd size)/2

Initial herd size = Current herd size - herd entries (purchases, births) + herd

exits (sales, deaths) in the last 12 months

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CHAPTER 2: HOUSEHOLD CHARACTERISTICS

A majority of the households surveyed were male headed (87%; 809/932). It is worth

mentioning at this point that most heads in female headed households had no male partners,

and therefore their household-lead role was by default. On average, female household heads

were 5 years older than their male counterparts (Table 4 & 4). In terms of education

attainment, majority of the household heads (>50%) had attained at least primary level

education. About 22% of the female and 37% of the male heads could not read or write

(Table 4 & 4).

Table 3: Age and education attainment of female household heads

District N Age

(years)

No formal and illiterate

(%)

No formal but literate

(%)

Primary school

(%)

Post primary

(%) Total

Lushoto 34 55.5 32.4 5.9 58.8 2.9 100

Mvomero 33 46.2 51.5 6.1 36.4 6.1 100

Handeni 45 54.1 28.9 11.1 60.0 0.0 100

Kilosa 6 45.2 50.0 0.0 16.7 33.3 100

Total 118 51.9 37.3 7.6 50.8 4.2 100

Table 4: Age and education attainment of male household heads

District N Age

(years)

No formal and illiterate

(%)

No formal but literate

(%)

Primary school

(%)

Post primary

(%) Total

Lushoto 184 49.0 5.4 3.3 85.9 5.4 100

Mvomero 204 45.0 34.0 7.8 43.2 15.0 100

Handeni 285 48.3 15.0 6.3 75.2 3.5 100

Kilosa 136 43.6 40.4 14.7 41.2 3.7 100

Total 809 46.8 21.9 7.4 63.8 6.9 100

2.1 Household size

The average household size in cattle keeping households was seven household

memberscompared to five in non-cattle keeping households (Table 5). Considering gender,

age and cattle keeping activity, the average number of men per household was equivalent to

that of women in each cluster, that is, two men and women in the cattle keeping households

and one man and woman in the non-cattle keeping households. Cattle-keeping households had

more children (7) compared to their counterparts who had an average of 5 children per

household (Table 5).

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Table 5: Average number of men, women and children per household

Cattle keeping households Non-cattle keeping households

District N Women Men Children Total N Women Men Children Total

Lushoto 165 1.6 1.6 2.4 5.6 56 1.3 1.3 1.5 4.1

Mvomero 245 2.0 2.0 3.4 7.4 60 1.3 1.2 2.0 4.5

Handeni 178 1.8 1.8 3.4 7.0 86 1.5 1.5 2.8 5.7

Kilosa 106 1.9 2.0 3.7 7.6 36 1.4 1.2 1.9 4.6

Total 694 1.8 1.9 3.2 6.9 238 1.4 1.3 2.1 4.9

2.2 Principal activities of household head

We now look at household members’ daily activities in all the study districts. Household

members are categorized as men, women and children based on gender and age. Focusing

on their primary roles, majority of the adult men and women were engaged in crop and

livestock activities, while most children of school going age were students (Table 6). Children

also participated in crop and livestock related activities, probably those that did not attend

school or during school holidays.

Table 6: Primary household activities by gender (count)

Activity Women (N=1600)

Men (N=1602)

Children3 (N=2731)

Crop farming 1041 930 58

Livestock & poultry keeping (including sales) 817 831 90

Trading in livestock and livestock products 1 14 0

Trading in agricultural products 3 11 0

Formal salaried employee 23 39 0

Business (non-agriculture) 70 102 1

Not working or unemployed 28 33 0

Old or retired 20 11 N/A

Infant (<6 years) 0 0 1205

Student/pupil 241 288 1267

Disabled 8 11 9

Casual laborer 6 17 0

Traditional healer 0 2 0

Domestic chores 1 0 2

No activity 0 0 55

2.3 Household membership in groups

Looking at household group affiliations, only 15% of cattle keeping households belonged to a

group. Households with at least one female member belonging to a group were slightly more

3 Children are those <15 years of age

17

(63%) than their male counterparts (Table 7). However, Kilosa registered a low household

female (9%) group engagement while Handeni had the highest (87%).

Table 7: Household group membership

District N

Households with at least one member in a

group (%)

Households with a man as a group

member (%)

Households with a woman as group

member (%)

Lushoto 165 20.0 57.6 63.6

Mvomero 178 11.8 51.7 58.6

Handeni 245 16.9 46.7 86.7

Kilosa 106 10.4 90.9 9.1

Total 694 14.8 56.3 63.1

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CHAPTER 3: ASSET OWNERSHIP AND MANAGEMENT

Asset ownership is often highly correlated with economic growth, poverty reduction and with a

reduction in vulnerability and risk exposure of the household4. Asset ownership is often viewed

as a form of investment or saving that improves the overall well-being of household members.

This chapter focuses on household ownership of assets. It also briefly reports on the role of

gender in ownership of assets. For the sake of our discussion, assets are disaggregated into

land and non-land assets. Non-land assets include housing, household and agricultural assets,

and livestock.

3.1 Land

Ninety seven percent of the households owned parcels of land with another 5% renting or

share cropping (Table 8). In some cases, some households owned land and also rented-in more

parcels of land. Mvomero and Kilosa had the highest proportion of farmers, 9% and 7%

respectively, renting-in land. Between one and five percent of households in Lushoto and

Kilosa utilized public land.

Table 8: Household access to and ownership of land

District N Households

owning land5 (%) Households on public land (%)

Households on rented/share-cropped

land (%) Lushoto 221 99.1 0.9 4.5

Mvomero 238 93.3 3.4 9.0

Handeni 331 99.7 0.0 0.9

Kilosa 142 93.0 4.9 7.0

Total 932 96.9 1.8 4.7

Overall, average land size owned was 15 acres per household and 3 and 25 acres in Lushoto

and Handeni, respectively (

Table 9). Only 7 households in Kilosa utilized public land and had the highest acreage of

public land (5.2 acres). None of the households in Handeni utilized public land. The average

land rented-in was one and three acres in Lushoto and Mvomero/Kilosa, respectively.

4 Deere, C.D and Doss, C.R (2005). The gender asset gap: what do we know and why does it matter?

(http://www.yale.edu/macmillan/faculty/papers/gender_asset_gap.pdf)

5 Own land include titled land and land owned but not titled

19

Table 9: Average land size (acres) owned

District

Owned land Public land Rented land

N Mean Median N Mean Median N Mean Median

Lushoto 221 3.0 2.0 2 0.4 0.4 10 0.9 0.9

Mvomero 331 17.6 8.3 8 2.4 2.0 21 3.2 2.0

Handeni 238 24.8 5.4 0 . . 3 1.5 0.3

Kilosa 142 13.8 5.0 7 5.2 5.0 10 3.4 2.8

Total 932 15.4 5.0 17 3.3 2.5 44 2.6 2.0

Considering land ownership in relation to gender, in most households (47%) in the four

districts, land was mainly owned by men. Forty two percent (42%) of households reported

joint ownership of land (Table 10). In total, 56% of the households had women owning land. In

Handeni, a high proportion of households (64%) reported joint ownership of land, while in

Kilosa, 59% of the households had men owning land.

Table 10: Land ownership by gender

District N Men (%) Women (%) Jointly (%)

Lushoto 221 48.4 19.9 39.4

Mvomero 331 29.6 9.7 29.9

Handeni 238 63.4 21.0 63.9

Kilosa 142 59.2 2.8 34.5

Total 932 47.2 13.9 41.5

3.2 Non-land asset

The non-land assets discussed in this section include housing, household and agricultural

equipment, and livestock. Each of these assets had a weight assigned and was adjusted for

age.

3.2.1 Housing

The CASHPOR6 house index (CHI), which uses external housing conditions, that is number of

rooms, floor material, wall material, roofing and the type of ownership, was used as a proxy

for poverty. Each quality attribute has a score of 0, 2 or 67. The house index describes the

6 CASHPOR is a network of 23 Grameen Bank replications in nine countries of Asia

7 Ownership No. of rooms Floor material Wall material Roofing

Borrowed=0 1 to 2 rooms=0 Earth=0 Earth/mud=0 Grass=0

20

quality of housing in four main categories (Table 11). Data from this study reveal that overall,

55% of the households surveyed had average to good housing (Table 11). About 68% of

households in Lushoto had average housing, while over 50% of households in Handeni and

Lushoto had poor housing.

Table 11: Proportion of households in the different housing quality categories

District N Classification of housing

Very poor housing (%)

Poor housing (%)

Average housing (%)

Good housing (%)

Total

Lushoto 221 2.7 19.5 68.3 9.5 100.0

Mvomero 238 2.1 46.2 27.7 23.9 100.0

Handeni 331 1.2 52.3 43.8 2.7 100.0

Kilosa 142 0.7 51.4 33.8 14.1 100.0

Total 932 1.7 42.8 44.0 11.5 100.0

3.2.2 Ownership of agricultural assets

Households were classified into three categories of ownership of agricultural assets: those that

owned domestic assets, transportation assets and farm assets. Under each category, asset

ownership was disaggregated by gender. Generally, domestic and transportation assets were

either owned by men or jointly owned. Farm assets were mainly jointly controlled (Table 12).

Table 12:Non-land asset index disaggregated by gender

District N

Domestic assets Transportation assets Farm assets

Men- owned

Women- owned

Jointly owned

Men- owned

Women-owned

Jointly owned

Men- owned

Women- owned

Jointly owned

Lushoto 221 3.9 3.1 2.7 2.7 0.1 0.9 1.1 0.6 1.9

Mvomero 237 4.0 2.5 3.4 8.7 0.4 9.2 1.9 0.4 4.6

Handeni 331 3.6 2.2 2.8 7.4 0.3 4.0 1.7 0.5 2.9

Kilosa 141 5.5 2.4 2.3 10.6 0.2 2.1 2.8 0.3 2.3

Total 930 4.1 2.5 2.8 7.1 0.3 4.3 1.8 0.5 3.0

*Domestic assets: cooker/gas stove, refrigerator, charcoal stove, radio, TV, DVD player, sofa

set/wooded chairs/couches, sewing machine, mosquito nets, solar panels, water tanks; *Transportation assets: car/truck, motorcycle, bicycle, animal drawn cart; *Farm assets: Hoes, spades/shovel, ploughs, biogas, power tiller sprayers pump and water pump.

3.3 Livestock assets

Livestock are valuable assets as they are treated as stored wealth, colloquially termed as

‘walking banks’. Looking at cattle ownership, both local and improved, majority were owned

Rented=2 2 to 4 rooms=2 Cement=2 Wood/Bamboo /Iron sheets=2 Iron sheets /Asbestos=2 Owned=6 >4 rooms=6 Tiles=6 Cement/Bricks=6 Tiles=6

21

by men mainly. Local goats, sheep, rabbits and donkeys were also mostly owned by men. The

remaining livestock were mainly jointly owned (

Table 13).

Table 13: Average numbers of livestock per household owned by men, women and jointly in

livestock keeping households

Livestock species

N

Male-owned Female-owned Jointly-owned

Mean Median Std. Dev Mean Median

Std. Dev Mean Median

Std. Dev

Local cattle 520 19.2 2.0 44.1 1.2 0.0 6.3 15.5 0.0 77.2

Improved cattle 212 2.8 0.0 17.8 0.2 0.0 0.6 1.4 0.0 3.8

Local goats 439 9.6 2.0 18.7 1.0 0.0 5.1 6.6 0.0 18.9

Exotic goats 18 1.6 0.0 2.5 0.2 0.0 0.5 1.9 0.5 3.7

Sheep 257 6.0 1.0 12.1 0.5 0.0 2.4 4.1 0.0 9.4

Local poultry 511 2.8 0.0 7.8 3.8 0.0 8.3 5.3 0.0 9.8

Exotic poultry 11 2.0 0.0 4.8 8.5 0.0 15.4 10.4 2.0 15.5

Rabbits 2 1.0 1.0 1.4 0.5 0.5 0.7 0.0 0.0 0.0

Donkeys 31 2.6 2.0 2.9 0.1 0.0 0.5 1.0 0.0 1.9

Pigs 10 1.7 0.0 3.9 0.6 0.0 1.6 11.9 5.0 17.2

Ducks 20 0.3 0.0 1.0 1.1 0.0 2.5 3.6 2.5 3.5

3.3.1 Cattle herd size

We further provide a summary of cattle kept by breed in the dairy households in the four

study districts. A majority of the cattle rearing households (>80%) in Mvomero, Handeni and

Kilosa districts raised local breeds, while majority of the households in Lushoto district (89%)

kept improved breeds. Local cattle heads in the sampled dairy households totaled 19,249

compared to 911 improved cattle (Table 14).

Table 14: Proportion of households raising cattle by breed

District N Households with local

cattle (%) Households with improved

cattle (%)

Lushoto 165 17.6 (53) 88.5 (342)

Mvomero 178 80.9 (6463) 24.2 (259)

Handeni 245 97.6 (7616) 4.9 (51)

Kilosa 106 93.4 (5117) 12.3 (259)

Total 694 73.6(19,249) 30.8 (911)

Figures in parentheses are number of cattle owned

If we look at the proportion of the different types of cattle kept, cows had the highest share in

the four districts (>30%) followed by replacement heifers (

22

Table 15). Bulls, perhaps kept for breeding purposes, constituted less than 6% of the herd.

Table 15: Types of cattle kept by district

Animal type

Lushoto Mvomero Handeni Kilosa

cattle owned

% cattle

owned %

cattle owned

% cattle

owned %

Bulls 20 5.1 454 6.8 1046 13.7 378 7.0

Castrated adult 2 0.5 166 2.5 168 2.2 330 6.1

Immature males 41 10.4 756 11.2 637 8.4 465 8.6

Cows 158 40.0 2585 38.5 2657 34.9 1803 33.5

Heifers 95 24.1 1287 19.1 1654 21.7 1080 20.1

Female calves 41 10.4 526 7.8 509 6.7 478 8.9

Male calves 27 6.8 368 5.5 409 5.4 363 6.8

Pre-weaning male 7 1.8 290 4.3 272 3.6 260 4.8

Pre-weaning female 4 1.0 290 4.3 260 3.4 219 4.1

Total 395 100 6722 100 7612 100 5376 100

Average cattle kept per household were as low as 2 TLUs in Lushoto and as high as 46 TLUs in

Kilosa (Table 16). Farmers in Lushoto district had the least TLUs probably because of their

small land holding that has necessitated intensive dairying. Half of the sampled households in

Mvomero, Handeni and Kilosa owned 19, 12, and 24 TLUs/household respectively.

Table 16: Total livestock units per household

District N Min Mean Median Std. Dev

Lushoto 165 0.8 2.1 1.9 1.4

Mvomero 178 0.8 33.5 18.7 43.4

Handeni 244 0.8 28.5 12.1 106.3

Kilosa 106 1.6 45.6 23.9 81.8

Total 693 0.8 26.1 9.6 75.4

The average numbers of improved cows was 2 cows per household in Mvomero and Kilosa

respectively. On the other hand, the average number of local cows was nine cows per

household in Mvomero and Kilosa (Table 17). Even though dairy households in Lushoto district

23

majorly kept the improved breed, Mvomero and Kilosa had more improved cows (2) per

household on average.

Table 17: Average number of cows kept by breed

District Cow keeping households

Pure/Cross Local

Mean Median Std. Dev Mean Median Std. Dev

Lushoto 114 1.2 1.0 0.9 0.2 0.0 0.5

Mvomero 235 1.9 0.0 2.6 14.8 8.5 20.4

Handeni 168 0.1 0.0 0.6 11.2 5.0 34.6

Kilosa 99 1.9 0.0 15.1 16.3 9.0 25.2

Total 616 0.7 0.0 6.2 11.0 5.4 26.4

CHAPTER 4: MILK PRODUCTION, UTILIZATION AND COST OF PRODUCTION

4.1 Daily milk production

On average, daily milk production from improved cows was 5 liters/cow per lactation. It was

4.1 liters in Mvomero and 6.3 liters in Kilosa. The average daily production from local cows

was 1.5 liters/cow (Table 18).

Table 18: Milk production per cow (by breed) per day per lactation

District Pure/cross Local

N Mean Median N Mean Median

Lushoto 38 4.2 3.0 4 2.4 1.8

Mvomero 6 4.1 4.0 174 1.4 1.3

Handeni 23 5.7 5.5 99 1.4 1.0

Kilosa 7 6.3 6.0 84 1.7 1.4

Total 74 4.9 4.1 361 1.5 1.2

4.2 Household milk utilization

Here, we consider households with lactating cow(s) at the time of the survey. Based on the

previous day’s household milk production, most of the milk produced in Lushoto was sold either

in fresh/fermented form, while it was consumed within the household, also in fresh or

fermented form, in the other districts (Table 19). No milk spoilage or rejection was reported in

the four districts. Only households in Lushoto reported giving milk to their calves (0.1 litres per

day) because of the intensive nature of dairy production in the area.

Table 19: Household utilization of daily milk production (in liters)

District

Households with lactating cow

Produced Sold

(fresh/fermented) Consumed

(fresh/fermented) Calves

Mean Median Mean Median Mean Median Mean Median

24

Lushoto 63 4.5 3.5 2.7 2.0 1.6 1.0 0.1 0.0

Mvomero 125 7.5 5.0 1.3 0.0 6.2 4.0 0.0 0.0

Handeni 190 5.6 4.0 2.4 0.3 3.2 2.0 0.0 0.0

Kilosa 93 14.6 10.0 5.6 2.0 9.0 6.0 0.0 0.0

Total 471 7.7 5.0 2.8 0.0 4.9 3.0 0.0 0.0

About 5% of the households with lactating cows reported no milk consumption at all (

Table 20). These households sold all the milk. Majority of these were from Handeni. About

33% (155/471) of households with lactating cow(s) prepared and consumed fermented milk.

Table 20: Milk consumption

District

Households with lactating cow

No milk consumption

Consume fermented only

Consume fresh milk only

Consume both fresh and fermented

Lushoto 63 4 1 49 9

Mvomero 125 2 1 98 24

Handeni 190 19 8 104 59

Kilosa 93 0 10 40 43

Total 471 25 20 291 135

4.3 Annual milk sales patterns

Information on annual milk sales was based on farmers’ ability to recall milk sales in the last

twelve months. The number of households selling milk increased between January and May

2012 in Mvomero and Kilosa (Figure 2). This increase in number of households corresponds to

the rainfall pattern in the two districts8 and hence feed availability. The pattern of households

selling milk in Lushoto and Handeni did not vary much over the year, but there was a slight

increase from August 2012.

8Anna Sikira, Honest Ndanu, Laswai, G.H and Nandonde, S.W. Report on participatory rural appraisal to inform

the three project of MoremilkiT, safe food fair food & milkIT projects in Morogoro and Tanga regions, Tanzania,

2012.

25

During the dry season, low feed and water availability in the extensive system causes

movement (also known as temporary transhumance) of animals to areas abundant with

pastures and water. This seasonal variation in feed availability has a direct influence on milk

sales pattern. This explains the variation in households selling milk throughout the year

especially in the extensive dairy production systems.

Figure 2: The annual pattern in milk sale.

4.4 Milk marketing

We now focus on the available milk market channels in the four districts and the price offered

by each. Local restaurants offered the best price per liter of milk (USD 54 cents) in Lushoto

and Handeni. Cooperatives in Mvomero offered the same price. Individual milk buyers

offered the highest price per liter of milk (USD 48) in Kilosa (Figure 3). Private milk traders

had the lowest offer in the 4 districts with the lowest being USD 28 cents in Handeni. This

corresponds to information gathered during the FGDs9.

9 Anna Sikira, Honest Ndanu, Laswai, G.H and Nandonde, S.W. Report on participatory rural

appraisal to inform the three project of MoremilkiT, safe food fair food & milkIT projects in Morogoro

and Tanga regions, Tanzania, 2012.

20

30

40

50

60

70

80

90

100

Co

un

t o

f d

airy

ho

use

ho

ld s

elli

ng

milk

Lushoto

Mvomero

Handeni

Kilosa

26

Figure 3: Milk market outlets and price

Looking at the proportion of milk marketed through each outlet, we see that individual

consumers/neighbors absorbed the largest share of milk followed by private milk traders

(Figure 4). There is an exception however, in Handeni where local restaurants were the second

major outlet. Access to major urban markets could be facilitated through the cold chain, which

in this case receives less than 20% of the surplus milk. This means only a small amount of milk

reaches the urban population, perhaps elucidating the low per capita milk consumption in

Tanzania reported by Njombe and others10.

10A.P Njombe, Y. Msanga, N. Mbwambo and N. Mkembe. The Tanzania Dairy Industry: Status,

Opportunities and prospects: Paper presented to the 7th African Dairy Conference and Exhibition at

MovenPick Palm Hotel, Dar es Salaam, 25-27 May 2011.

0

10

20

30

40

50

60

Lushoto Handeni Mvomero Kilosa

37

30

48 48

34 28

44 40

54 54

45

31

40

54

35 34 36

Ce

nts

pe

r lit

re (

$)

Individual consumers Private milk-traders Hotel/canteen

Co-op with chilling plants Privately owned chilling plants Co-op without chilling plants

27

Figure 4: Milk marketing channels.

4.4.1 Marketing of fermented milk

Fermented milk was prepared by only 34% (159/471) of households with a lactating cow.

Most of the households processing milk (96%) consumed it at household level. Sale of

fermented milk was done in two districts only, Handeni and Kilosa. Only about 4% of

households that fermented milk sold the fermented milk product. The main market was

individual consumers/neighbors. The price of fermented milk ranged between USD 27 and 38

cents per liter in Kilosa. In Handeni, it was USD 54 cents per liter.

0

20

40

60

80

100Pe

rcen

t fre

sh m

ilk so

ld

District

Privately owned chilling plants

Co-op with/without chillingplants

Hotel/canteen

Private milk-traders

Individual consumers

28

CHAPTER 5: BREEDING, ANIMAL HEALTH AND OTHER DAIRY-RELATED SERVICES

Cattle productivity in Tanzania remains lower than expected and this has been attributed to

both genetics and environmental factors. Livestock diseases have also contributed to low

productivity and economic losses. In this chapter, we look at the breeding methods used, their

availability, sources and associated costs. We also look at the common animal health

preventive measures and treatment of commonly occurring diseases, the availability of

services, service providers and the cost of animal health services.

5.1 Breeding

5.1.1 Availability and use of insemination services

Over 80% of the household interviewed reported availability and use of bull insemination

(Table 21). Artificial insemination was not a popular method of breeding in the four study

districts, with less than 10% and 2% of the respondents reporting its availability and use

respectively. This could largely be explained by breed of cattle kept and the production

system in use.

Table 21: Proportion of households reporting availability and use of breeding services

District N

% reporting availability of service

% reporting use of service

AI Bull service AI Bull service

Lushoto 165 17.6 94.5 1.8 64.2

Mvomero 178 12.9 97.2 6.2 84.3

Handeni 245 4.1 97.6 0.4 82.9

Kilosa 106 0.9 98.1 0.0 93.4 Total 694 9.1 96.8 2.2 80.4

5.1.2 Cost of insemination

Respondents were further probed about the type of services they frequently used for

insemination. About 98% (558/569) of farmers that had serviced their cattle in the previous

year used the bull service. Half of those using the bull service did not incur any cost as they

either used their own bull, which they did not consider as an expense or used another farmer’s

bull at no cost (

Table 22). The cost of insemination was highest in Lushoto for both artificial insemination (USD

18.3 per service) and bull service (USD 2.7 per service). None of the dairy households in

Kilosa used artificial insemination.

29

Table 22: Cost of insemination service (USD per service)

District Artificial insemination Bull service

N Mean Median N Mean Median

Lushoto 3 18.3 14.7 106 2.7 2.0

Mvomero 10 7.4 6.7 150 0.7 0.0

Handeni 1 13.4 13.4 203 0.1 0.0

Kilosa 0 . . 99 1.0 0.0

Total 14 10.1 6.7 558 0.9 0.0

Looking at the sources of services and/or service providers, a majority of dairy households

used bulls from their own (339 households) or other farms (296 households). A few of those

using AI sourced mainly from private service providers (Table 23).

Table 23: Count of dairy households using various sources of insemination/service providers

District N Own bull

Other farmer bull

Bull schemes*

Private AI provider

Government/public AI

Coop/group AI provider

Lushoto 165 3 142 3 1 0 1

Mvomero 178 121 45 1 4 1 0

Handeni 245 129 97 1 0 0 0

Kilosa 106 86 12 1 0 0 0

Total 694 339 296 6 5 1 1

*including government bull

Various initiatives to improve the local breed in Tanzania have been tried as far back as in

the 20th century with little or no success. Such initiatives include the village bull centers, the

government/project bull centers, NGO breeding units and the pass-on initiative. NGO

initiatives have registered slow growth, while the bull centers have collapsed due to poor

management11. On the other hand, uptake of AI has been crippled by lack of the necessary

infrastructure for an extensive AI scheme.

5.2 Animal health service

5.2.1 Availability of animal health service

Availability of deworming and tick control services was reported by over 80% of the

households. Availability of vaccination services was reported by less than half of the

11 Kurwijila 2001. Evolution of dairy policies for smallholder production and marketing in Tanzania. Proceedings of a South–South workshop held at National Dairy Development Board (NDDB), Anand, India, 13–16 March 2001

30

households (49%) while that of treatment services was reported by slightly over a half of the

households (Table 24). The proportion reporting use of these services was also high for

anthelmintic and tick control (72%) unlike vaccination and curative where use was low (<44)

and demand is mostly on need-to basis.

Table 24: Proportion of households reporting availability and (use) of animal health services

District N Anthelmintic Tick control Vaccination Curative

Lushoto 165 86.7 (73.3) 57.0 (46.1) 57.0 (35.8) 40.6 (18.8)

Mvomero 178 82.0 (75.8) 94.4 (88.8) 46.6 (26.4) 63.5 (52.8)

Handeni 245 80.0 (69.0) 93.1 (91.4) 54.3 (34.7) 51.0 (42.9)

Kilosa 106 80.2 (73.6) 93.4 (92.5) 29.2 (26.4) 70.8 (67.0)

Total 694 82.1(72.5) 84.9 (80.1) 49.1(31.6) 54.8 (43.4)

5.2.2 Commonly treated diseases

Farmers were asked to provide information on diseases that their cattle had been most

recently treated for. Majority of the farmers listed tick-borne diseases (39%) and other vector

borne diseases (21%) as the main diseases treated (Table 25).

Table 25: Count of diseases commonly treated against in the previous 12 months

District

Households treating

Tick-borne

Other vector borne

General frequent infection

Don't know

Notifiable

Others*

Management-related

Skin problem

Lushoto 31 41.9 3.2 3.2 32.3 0.0 12.9 22.6 6.5

Mvomero 105 40.0 19.0 20.0 4.8 9.5 1.9 0.0 1.0

Handeni 94 44.7 24.5 14.9 16.0 8.5 6.4 2.1 2.1

Kilosa 71 26.8 26.8 26.8 9.9 9.9 2.8 1.4 0.0

Total 301 38.5 20.9 18.3 12.3 8.3 4.7 3.3 1.7

* include injuries, mineral deficiency, non-specific symptoms

5.2.3 Administration of animal health services

Farmers also provided information on type of service providers engaged in preventive and

curative services. In Kilosa district, all animal health services were mainly administered by the

farmer with ‘professional’ advice. Similarly, worm control was mainly self-administered with or

without ‘professional’ advice in the other three districts (Table 26). Vaccination was carried out

by a government health service provider in Lushoto, Handeni and Mvomero. Tick control

services were also provided mainly by a government health service provider in Lushoto as

reported by over 60% of the households. Despite availability of the service as shown earlier

in Table 24, treatment was self-administered in the other districts except in Lushoto where this

was mainly performed by government health personnel (Table 26).

31

Table 26: Proportion of dairy farmers reporting having used animal health services and type

of service providers used in the previous 12 months

Animal health service/service provider Proportion of dairy farmers by district


Anthelmintic (n=121) (n=135) (n=169) (n=78)

Self/neighbour with professional advice 38.8 32.5 53.3 73.1

Self/neighbour with no professional advice 14.9 38.5 45.9 26.9

CAHSP 8.3 1.2 0 0

Government health service provider 37.2 7.7 25.9 0

Cooperative/project veterinarian 0.8 0 0 0

Tick control (n=76) (n=157) (n=224) (n=98)



CAHSP 5.3 0.4 8.3 1

Government health service provider 31.6 2.7 45.9 0


Community/private dip 1.3 0 21 0

Vaccination (n=59) (n=46) (n=85) (n=28)



CAHSP 28.8 13 14.1 10.7

Government health service provider 61 63 75.3 32.1


Private provider 0 0 0 3.6

Curative (n=31) (n=94) (n=105) (n=69)



CAHSP 16.1 8.6 10.6 1.4

Government health service provider 58.1 17.1 26.6 1.4

Project/NGO staff 3.2 0 0 0


5.2.4 Decision making on administration of animal health services and selection of

service providers

Considering gender involvement in decision making on issues regarding animal health, in most

households (>50%) men made decision on use and the service providers to be contacted for

any of the services in the four districts (Table 27). Joint decision making was reported in at

least 15% of the households. Other non-household members were also involved in decision

32

making especially in vaccination. This could be attributed to involvement of the government in

response to disease control.

Table 27: Proportion of gender involvement in decision making on administration of animal

health services

Animal health service/decision maker Decision makers by gender (proportion)


Anthelmintic (n=135) (n=144) (n=188) (n=83)

Men 59.3 75.7 71.8 80.7

Women 14.8 6.3 10.6 1.2

Jointly 24.4 17.4 16.5 15.7

Non-hhd member 1.5 0.0 0.5 0.0

Tick control (n=83) (n=163) (n=225) (n=98)

Men 67.5 74.2 63.1 85.7

Women 10.8 7.4 11.6 1.0

Jointly 18.1 17.2 17.3 12.2


Vaccination (n=77) (n=76) (n=138) (n=36)

Men 55.8 53.9 56.5 69.4

Women 11.7 9.2 9.4 0.0

Jointly 23.4 19.7 18.8 19.4

Non-hhd member 9.1 14.5 14.5 2.8

Curative (n=38) (n=101) (n=112) (n=74)

Men 60.5 76.2 58.0 81.1

Women 13.2 5.9 9.8 0.0

Jointly 21.1 14.9 29.5 16.2


5.2.5 Cost of animal health services

Farmers using animal health services were asked to provide information on cost incurred in the

last 12 months. Given that in most households farmers administered most of the services single-

handedly or with occasional assistance, the cost involved was mainly that of drugs. The

expertise of the ‘professional’ advisers could not be ascertained. The pooled annual cost of

services (tick control, deworming, vaccination and treatment) ranged from USD 10 to USD 348

with households in Lushoto incurring the lowest and those in Mvomero the highest (

Table 28).

33

Table 28: Annual cost of animal health services

District Number of Households using animal health services

Mean (USD)

Median (USD)

Std. Dev

Lushoto 139 10.1 5.7 12.9

Mvomero 168 347.7 100.5 1812.3

Handeni 238 128.1 24.1 507.5

Kilosa 104 326.8 117.3 650.3

Total 649 191.5 30.8 1012.8

5.3 Other services available in the study sites

Respondents were asked to provide information on other services available in the study

districts including extension, financial and other social utilities. Most of these services were not

common in the study districts (Table 29). Livestock (including cattle) extension services were

somewhat popular in Lushoto and Mvomero districts. Over half of the households interviewed

in Lushoto (55%) and 43% of households in Mvomero reported availability of cattle extension

services. Extension services for livestock in general were reported by 49% and 41% of the

dairy households in Mvomero and Lushoto respectively.

Table 29: Proportion of farmers reporting availability of extension and other services

Type of service

Proportion of farmers reporting availability of services

Lushoto (n=165)

Mvomero (n=173)

Handeni (n=250)

Kilosa (n=106)

Extension visits

Livestock 41.2 49.1 29.2 23.6

Cattle 54.5 42.8 31.2 19.8

Crop 16.4 12.7 2.8 7.5

Training

Livestock 18.8 16.2 9.6 7.5

Cattle 17.0 15.6 7.2 5.7

Crop 10.3 5.8 1.2 2.8

Other type of information 11.5 9.8 12.0 11.3

Financial services

Savings 23.6 24.3 10.0 1.9

Credit 21.2 20.8 8.4 0.0

Health insurance 8.5 8.1 9.2 6.6

34

Domestic/home insurance 0.0 0.6 0.0 0.0

Electricity/piped water

National grid 21.2 26.6 22.4 16.0

Solar 9.7 17.9 19.2 20.8

Piped water 21.2 24.3 31.2 20.8

CHAPTER 6: CATTLE FEEDING SYSTEMS AND ALLOCATION OF LABOR TO DAIRY ACTIVITIES

5.1 Feeding systems and source of livestock fodder/feed

In this chapter we seek to understand where farmers obtain cattle feeds, cattle feeding

systems in use, source of information regarding livestock fodders, planting materials and cost

implications. We also review distribution of labor by gender and weekly time allocation to

dairy activities.

Majority of the households rearing local breeds in all the study districts, except Lushoto,

grazed their cattle irrespective of the season. During the dry spell, a high proportion of

households notably Mvomero (42%) and Kilosa (23%) had their cattle on transhumance. In

Lushoto, the cattle rearing system was purely of a sedentary nature. Improved breeds in all

the sites were mainly stall fed except in Kilosa where all cattle breeds were mainly grazed

(Figure 5). Transhumance was common during the dry seasons.

35

Figure 5: Proportion of households using different cattle feeding systems for local and exotic breeds.

5.2 Source of livestock feed

Lushoto district had the highest proportion of farmers growing fodder (83%) and the highest

proportion of land under fodder (22%), perhaps due to the stall feeding system used in these

households. Farmers in this district mainly grew napier (98 households) and pasture grass (59

households). None of the farmers in Handeni reported growing fodder or pasture (Table 30).

Table 30: Proportion of land under fodder and number of farmers growing fodder

District

N

% households growing fodder/ pasture

% land under fodder

Number of farmers growing fodder/pasture grasses

Napier grass

Planted grasses

Fodder shrubs

Other fodder legumes

Sugar cane

Fodder maize

Lushoto 165 83.0 22.2 98 59 5 3 2 2

Mvomero 178 1.7 3.3 1 0 3 0 0 0

Handeni 245 0.0 0.0 0 0 0 0 0 0

Kilosa 106 1.9 5.0 2 0 0 0 0 0

0

20

40

60

80

100

Dry Wet Dry Wet Dry Wet Dry Wet

Lushoto (n=146) Handeni (n=12) Mvomero (n=43) Kilosa (n=13)

Perc

ent ho

use

hold

s

Exotic cattle feeding system

Grazing (with or without stall feeding) Mainly stall feeding Transhumance (some/all animals)

0

20

40

60

80

100

Dry Wet Dry Wet Dry Wet Dry Wet

Lushoto (n=29) Handeni (n=239) Mvomero (n=144) Kilosa (n=99)

Perc

ent ho

use

hold

s

Local cattle feeding system

Grazing (with or without stall feeding) Mainly stall feeding Transhumance (some/all animals)

36

Total 694 20.5 21.6 101 59 8 3 2 2

5.3 Source of planting materials

The respondents provided information about sources of fodder/pasture planting materials.

Napier and pastures grass, the main grown livestock fodder, were recycled from either their

own farms (as reported by 48% and 53% of respondents, respectively) or from another

farmer (Figure 6). Planting materials for fodder shrubs were mainly recycled from other

farmers (reported by 50% of respondents).

Figure 6: Source of planting material.

Apart from growing fodder, we sought to understand other sources of feed material. Data on

farmers purchasing fodder and utilization of crop residues was obtained. We found that

Lushoto had the highest proportion of cattle keepers (42%) purchasing fodder (Table 31Error!

Reference source not found.). The remaining three districts had very few farmers purchasing.

Fodder was purchased only for a period of 3 to 5 months in Mvomero, Kilosa and Handeni.

Table 31: Proportion of dairy farmers purchasing fodder and number of months in which fodder was purchased

District N % farmers

purchasing fodder No. of months/year

purchased

Lushoto 165 41.8 4.0

Mvomero 178 0.6 3.0

Handeni 245 3.3 5.3

Kilosa 106 2.8 3.0

Total 694 11.7 4.0

0

20

40

60

80

100

Napiergrass

(n=101)

Plantedgrasses(n=57)

Foddermaize(n=2)

Foddershrubs(n=8)

Otherfodderlegumes(n=3)

Sugarcanegrass(n=2)

Total(n=173)

perc

ent ho

use

hold

s

Type of fodder

Recycled from own farm Recycled from other farmers Improved seeds Other

37

Regarding utilization of crop residues, over 50% of cattle keepers utilized it as a source of

feed. Lushoto had the highest proportion of farmers (89%) using crop residues (Table 32).

Over 90% of households utilizing residues obtained it from own farms while 15% purchased

from other farmers. On average, farmers bought crop residues for about 4 months in a year.

Table 32: Proportion of dairy farmers feeding crop residues and their sources

District N % using crop

residues % growing

crop residues % purchasing crop residues

No. of months/year purchased

Lushoto 165 88.5 93.2 23.3 4.0

Mvomero 178 48.3 95.3 11.6 3.3

Handeni 245 45.7 95.5 8.9 2.9

Kilosa 106 30.2 93.8 12.5 2.3

Total 694 54.2 94.4 15.4 3.6

Lushoto had the highest proportion of farmers (64%) supplementing dairy cattle with

commercial feeds (

Table 33). Bran was the main feed supplement used by most dairy farmers (73%). Mineral

supplements were only administered by 27% of the dairy households. Supplementation was

done for about 7 months in a year.

Table 33: Proportion of dairy farmers using feed and mineral supplements

District N Concentrate Bran

Mineral

blocks

Commercial daily

meal

Oilseed by-

products Maize germ

Agro-industrial

by-products

No. of months/year used

Lushoto 165 64.2 72.6 28.3 13.2 7.5 3.8 1.9 6.7

Mvomero 178 22.5 72.5 32.5 10.0 15.0 5.0 0.0 8.8

Handeni 245 4.9 91.7 8.3 8.3 0.0 0.0 0.0 6.9

Kilosa 106 6.6 57.1 14.3 14.3 28.6 0.0 0.0 7.4

38

Total 694 23.8 73.3 27.3 12.1 9.7 3.6 1.2 7.2

We now look at expenditure on livestock feed. The highest cost of feed was USD 158 incurred

in the purchase of feed concentrate followed by USD 133 for fodder (Table 34). The annual

cost of fodder planting materials was low (USD 7.5) and this could be a result of farmers

using recycled crop, which was not regarded as an expense.

Table 34: Annual cost of planting material for feed/fodder

District Planting material Crop residues Fodder Concentrate

N Mean N Mean N Mean N Mean

Lushoto 138 7.1 146 11.6 69 103.5 106 91.5

Mvomero 3 33.5 86 9.8 8 270.6 40 314.6

Handeni 0 . 112 7.8 1 603.2 11 226.0

Kilosa 2 1.7 32 15.5 3 277.0 7 155.1

Total 143 7.5 376 10.4 81 132.6 164 157.6

5.4 Information regarding fodder

Majority of farmers growing fodder/pastures sought information from extension agents or

relied on own or other farmers’ experience (Table 35). Other potential sources of information

such as NGOs, private extension service providers, research organizations and cooperatives

were not significant.

Table 35: Source of information on fodder

Fodder/pasture type N

Number of households using information source

Extension agent

Farmer experience

NGO/project

Research institute

Private extension

Coop/group

Napier grass 101 49 41 4 3 3 1

Planted grasses 56 33 16 2 2 2 1

Fodder maize 2 1 0 1 0 0 0

Fodder shrubs 8 5 2 0 1 0 0

Other fodder legumes 3 3 0 0 0 0 0

Sugar cane grass 2 0 1 1 0 0 0

Total 172 91 60 8 6 5 2

5.5 Labour and time allocation to dairy activities

Data on labour included amount of labor used, type of activity, gender of worker and

whether worker was part of the family or hired. Data collected was evidence of the

differential role of gender in dairy related activities. Labor for cattle-related activities was

mainly provided by household men or women and reliance on hired/non-household member

was quite minimal (Table 36). Grazing took the greatest share of time especially in Handeni,

39

Mvomero and Kilosa where grazing was the main feeding system for local breeds, the

predominant breed in these sites. Children also spent time assisting in grazing cattle.

40

Table 36: Average labor time (hours) allocated to dairy activities by gender over one week period

District/ Activity N

Household labour Non-household labour

Lushoto

Men Women Children (<15 years) Men Women

mean median mean median mean median mean median mean median

Grazing 11 17.6 6.0 1.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Feeding (collection & preparation) 157 13.3 7.0 11.4 7.0 1.7 0.0 0.5 0.0 0.0 0.0

Providing water to the animals 156 2.3 0.5 3.5 3.0 0.8 0.0 0.0 0.0 0.0 0.0

Cleaning animal shed/shelter 144 4.2 2.0 3.8 1.0 0.2 3.8 1.0 0.0 0.0 0.0

Collection of farm yard manure 111 5.5 2.0 3.1 0.0 1.2 0.0 0.3 0.0 0.0 0.0

Milking 73 2.5 0.3 2.5 1.7 0.1 0.0 0.1 0.0 0.0 0.0

Selling milk 53 4.5 1.0 2.7 0.2 0.6 0.0 0.0 0.0 0.0 0.0

Selling animals/animal products (except milk) 13 4.3 1.0 4.3 0.5 0.0 0.0 0.0 0.0 0.0 0.0

Disease control/caring for sick animals 47 7.2 2.0 1.3 0.0 0.1 0.0 0.0 0.0 0.0 0.0

Mvomero


N mean median mean median mean median mean median mean median

Grazing 141 54.7 49.0 1.6 0.0 20.3 0.0 13.5 0.0 1.5 0.0





Milking 130 0.7 0.0 11.2 7.0 0.9 0.0 0.1 0.0 0.0 0.0

Selling milk 40 1.3 0.0 7.4 3.3 0.3 0.0 0.0 0.0 0.0 0.0



Handeni



Grazing 234 52.6 49.0 11.9 0.0 12.7 0.0 8.0 0.0 0.0 0.0


41




Milking 194 1.7 0.0 6.5 3.5 0.7 0.0 0.5 0.0 0.0 0.0

Selling milk 54 2.1 0.0 6.5 0.5 0.0 0.0 0.4 0.0 0.0 0.0



Kilosa



Grazing 102 53.7 56.0 3.2 0.0 16.4 0.0 4.7 0.0 0.0 0.0





Milking 92 3.4 0.0 10.8 7.0 0.7 0.0 0.0 0.0 0.0 0.0

Selling milk 34 1.9 0.0 10.5 3.5 0.4 0.0 0.0 0.0 0.0 0.0



42

CHAPTER 7: ROLE OF LIVESTOCK IN HOUSEHOLD FOOD SECURITY

In this chapter, we discuss the contribution of livestock to household food security among poor

smallholder dairy households. Livestock can contribute to food security through two ways:

through increased consumption of animal source food and increased incomes that can be used

to purchase additional food for the household. We use two indicators of food security,

namely, the household dietary diversity score (HDDS) and FCS. In chapter eight, we shall cover

the contribution of livestock to household income.

7.1 Household Dietary Diversity Score

The HDDS is the sum of all food groups consumed over a period of 24 hours within a

particular household divided by total number of food groups considered. It is meant to

provide an indication of household economic access to a variety of foods. Food consumed

elsewhere, for instance in restaurants and parties is not included in computation of the dietary

score as it does not represent the household’s access to food. For the HDDS, 12 food groups

are considered. These include cereals, roots and tubers, vegetables, fruits, meat/poultry/offal,

eggs, fish and other seafood, pulses/legumes/nuts, milk and milk products, oils and fats,

sugar/honey/sweets, and spices, condiments and beverages.

The average HDDS for households in Lushoto, Mvomero and Handeni district was 0.5.

Households in Kilosa district had the lowest (0.4) HDDS (Table 37). Half of the households in

the three districts had access to 50% of the food groups while half of the households in Kilosa

had access to 40% of the food groups.

Table 37: Household dietary diversity score by district

District N Mean Median St.Dev

Lushoto 220 0.5 0.5 0.2

Mvomero 238 0.5 0.5 0.1

Handeni 331 0.5 0.5 0.2

Kilosa 142 0.4 0.4 0.1

Total 931 0.5 0.5 0.2

7.2 Food Consumption Score

The FCS is a frequency-weighted dietary diversity score calculated using the frequency of

consumption of different food groups consumed by a household seven days before the survey.

To calculate the FCS, we consider 9 main food groups each weighted by nutrient densities

43

estimated by the World Food Program12. The food groups include main staples; vegetables;

fruits pulses, meat and fish; milk; oil; sugar and condiments. Generally, over 90% of the

households were within the acceptable food group consumption threshold (Table 38).

Table 38: Proportion of households within FCS

District N

Food consumption groups

Poor FCS

(0-21)

Borderline FCS

(21.5-35)

Acceptable FCS

(>35)

Total

Lushoto 221 5.9 8.1 86.0 100.0

Mvomero 238 0.8 4.6 94.5 100.0

Handeni 331 2.4 8.5 89.1 100.0

Kilosa 142 0.7 7.0 92.3 100.0

Total 932 2.6 7.2 90.2 100.0

7.2.1 Contribution of meat, fish and milk to the FCS

This is calculated as a proportion of the total FCS contributed by meat & fish and milk.

Overall, milk contributed to 26%, while meat and fish contributed 20% of the total FCS (Table

39).

Table 39: Percentage contribution of meat, fish and milk to the food consumption score

District N Household

FCS (total)

Milk FCS

Contribution of milk to household

FCS (%)

Meat & fish FCS

Contribution of meat & fish to household

FCS (%)

Lushoto 221 60.5 11.1 18.3 15.6 25.7

Mvomero 238 61.4 17.0 27.6 10.0 16.2

Handeni 331 63.8 17.5 27.4 12.4 19.5

Kilosa 142 58.0 19.6 33.8 10.0 17.2

Total 932 61.5 16.2 26.3 12.2 19.8

12 WFP vam, 2008. Food consumption analysis. Calculation and use of the food consumption score in food securityanalysis.

44

CHAPTER 8: INCOME AND MANAGEMENT OF HOUSEHOLD INCOME

In this chapter we look at the various household sources of income and management of the

income. In the dairy households, we look at income from sale of milk (both fresh and

fermented), sale of cattle and other sources of income, which are non-dairy related. The later

also applies to the non-dairy households.

8.1 Income from sale of milk

Table 40 below provides summary statistics of revenue generated from sale of fresh milk by

district. The statistics were computed for dairy households keeping cows. The average annual

income from sale of milk was USD 497 in Kilosa with an annual maximum sale of USD 5791.

Dairy households in Lushoto reported the lowest income from this source (USD 89). Half of the

dairy households keeping cows had no income from milk.

Table 40: Annual income from sale of fresh milk (USD)

District N Min Median Mean Max Std Dev

Lushoto 115 0 0 89.4 1664.9 232.2

Mvomero 168 0 0 135.8 3860.6 432.6

Handeni 236 0 0 157.9 3619.3 447.6

Kilosa 98 0 0 496.6 5790.9 966.6

Total 617 0 0 192.9 5790.9 549.9

Fermented milk was sold in two districts only, Handeni and Kilosa with an average annual sale

of up to USD 6 and USD 16, respectively (Table 41).

Table 41: Annual income from sale of fermented milk (USD)

District N Min Median Mean Max Std. Dev

Lushoto 115 0 0 0.0 0.0 0.0

Mvomero 168 0 0 0.0 0.0 0.0

Handeni 236 0 0 6.0 1266.8 82.7

Kilosa 99 0 0 15.6 1351.2 137.0

Total 618 0 0 4.8 1351.2 74.9

8.1.2 Management of household income from milk

Majority of the households selling milk did so in the morning. The income gained from

of morning milk was managed by the household female. On the other hand only 45

households sold milk in the evening and majority of the revenue collected was

jointly especially in Handeni and Kilosa (

Table 42).

45

Table 42: Management of milk income (percentage by gender)

District

Morning milk Evening milk

N Men (%)

Women (%)

Jointly (%)

Non-household member (%)

N Men (%)

Women (%)

Jointly (%)

Lushoto 44 29.5 31.8 36.4 2.3 8 37.5 37.5 25.0

Mvomero 32 12.5 43.8 43.8 0.0 23 21.7 30.4 47.8

Handeni 95 23.2 37.9 36.8 2.1 11 0.0 27.3 72.7

Kilosa 49 28.6 57.1 14.3 0.0 3 0.0 33.3 66.7

Total 220 24.1 41.8 32.7 1.4 45 17.8 31.1 51.1

8.2 Income from sale of cattle

More than 50% of the dairy households in Mvomero, Handeni and Kilosa reported an income

from sale of cattle in the previous year (Table 43). The average annual revenue in Mvomero

was the highest at USD1380 followed by Kilosa (USD 974). Lushoto reported the lowest

income from sale of cattle, estimated at USD 103.

Table 43: Annual income from sale of cattle (USD)


Lushoto 165 0.0 0.0 103.3 4021.4 341.9

Mvomero 178 0.0 368.6 1379.7 23,793.6 3235.2

Handeni 245 0.0 134.0 412.4 10,053.6 1040.6

Kilosa 106 0.0 469.2 973.8 7573.7 1427.1

Total 694 0.0 154.2 672.8 23,793.6 1906.3

Respondents were asked for reasons for disposing of their cattle. A majority of farmers

(>90%) cited the need to meet household expenses, either planned or emergencies, as the

main reason for selling cattle. Other reasons mentioned include culling the unproductive and

sick from the herd, and involvement in livestock trading among others.

0

20

40

60

80

Planned expenses Emergencyexpenses

Others*

Pe

rop

ort

ion

ho

use

ho

lds

(%)

Purpose of selling


46

*include trading as a business, culling unproductive and sick, accident, replacements etc.

Figure 7: Reasons for sale of cattle

8.3 Income from other sources

Other activities generating household income included sale of own livestock and livestock

products other than cattle, sale of own agricultural products, trade in non-own livestock and

livestock products and agricultural products, formal employment, businesses, off-farm labor,

sale of products of natural resources, rented-out land/sharecropping and remittances. The

overall average household income from these sources was USD 1013 (Table 44). Households

in Mvomero had the highest mean income from other sources (USD 1353) with a maximum of

USD 40,215 followed by Kilosa (USD 1049).

Table 44: Annual income from other sources (USD)


Lushoto 220 0 234.6 882.2 34,316.4 3084.9

Mvomero 238 0 454.4 1352.9 40,214.5 3362.4

Handeni 331 0 281.5 840.0 36,863.3 2704.1

Kilosa 140 0 452.4 1049.0 20,107.2 2126.3

Total 929 0 321.7 1012.9 40,214.5 2907.8

47

CHAPTER 9: CATTLE MORTALITY

Cattle mortality is one on the major setbacks in dairy production. Of concern in any dairy

enterprise is calf mortality as calves are the main source of replacement herd. This section

focuses on herd exits through death, segregating this data by genotype and age. We also

look at the causes of death.

9.1 Cattle mortality rates

The mortality rates are estimates from farmers’ retrospective report on cattle mortality on the

farm and associated causes. Table 45 summarizes the various causes of death as presumed by

the respondents. Most cattle deaths were mainly associated with diseases (996) and long

spells of drought (922). In other words, there was an average of 8 heads of cattle succumbing

to either disease or drought in every dairy household.

Table 45: Number of households reporting death and (heads of cattle succumbing to associated causes)

District Households reporting death (count)

Disease Drought Injury Natural death

Other Poisoning

Lushoto 9 6 (6) 0 1 (1) 1 (1) 1 (1) 0

Mvomero 103 50 (289) 47 (775) 12 (18) 2 (2) 2 (2) 1 (4)

Handeni 112 86 (564) 12 (64) 12 (26) 1 (1) 4 (6) 2 (2)

Kilosa 57 35 (137) 13 (83) 7 (10) 3 (7) 0 2 (2)

Total 281 177 (996) 72 (922) 32 (55) 7 (11) 7 (9) 5 (8)

Table 46 provides a summary of the diseases leading to death of cattle in the previous year

as perceived by the respondents. The main diseases implicated for most deaths were tick-

borne diseases (by 69/177 of dairy households) especially in Mvomero, Handeni and Kilosa.

A number of farmers (61/177) did not know the actual disease their cattle succumbed to. In

Lushoto, half of the farmers reporting deaths as a result of disease did not know the actual

disease (Table 47).

Table 46: Number of households reporting diseases attributable to cattle mortality

District

Households reporting death due to disease (count)

Tick-borne

Don’t know

Other vector borne

General infection

Notifiable

Other*

Lushoto 6 0 3 0 0 0 3

Mvomero 50 22 15 5 8 5 Handeni 86 33 34 8 9 3 5

48

Kilosa 35 14 9 10 5 2 Total 177 69 61 23 22 10 8

*other includes brucellosis, birth complications

Table 47 provides a summary of crude mortality rates of local and exotic cattle by district.

Overall, the cattle mortality rate was at 7.8%. At district level, Mvomero had the highest

cattle mortality rate of 12.3%, attributed to the high number of deaths resulting from drought,

while Lushoto reported the lowest rate of 2.6%. Mortality rate by genotype was highest in

Handeni among the exotic breeds (10.6%) and in Mvomero among the local breeds (13.3%).

Table 47: Cattle mortality rates over one year period

District Cattle

keepers No. of dead

cattle (including calves)

Average cattle mortality rate

(All)


(Exotic)


(Local)

Lushoto 165 9 2.6 2.9 0.0

Mvomero 178 1090 12.3 6.6 13.3

Handeni 245 663 7.9 10.6 7.5

Kilosa 106 241 8.0 1.0 8.4

Total 694 2003 7.8 4.0 8.8

Table 48 presents a summary of mortalities attributed to diseases. This summary shows the

impact of disease on dairy production. The overall mortality rate was 4.6%. Exotic cattle

registered a lower mortality rate of 2.3% compared to the local breed (5.1%). At the district

level, Lushoto reported the lowest mortality due to disease perhaps due to better access to

animal health care as indicated in Error! Reference source not found.Table 24).

Table 48: Cattle mortality rates caused by diseases over one year period

District

N

No. succumbing to disease (including

calves)


(All)


(Exotic)


(Local)

Lushoto 165 6 1.7 1.7 0.0

Mvomero 178 289 5.4 3.3 5.6

Handeni 245 564 5.9 5.5 5.7

Kilosa 106 137 4.6 1.0 4.8

Total 694 996 4.6 2.3 5.1

Looking at herds which had calves (up to 12 months of age), overall, households with herds had lower calf mortality rate (1.4%) as compared to the local herds (6.0%). Exotic mortality of 5.8% was reported in Mvomero district only (

Table 49: Calf mortality rate

49

Table 49). Among the local calves, the highest mortality rate was reported in Mvomero (8.1%)

while Kilosa recorded the lowest mortality rate of 2.3%. No calf mortalities were reported in

Lushoto districts.

Table 49: Calf mortality rates by genotype

District

Exotic calves Local calves

No. of households with exotic calves

No. of dead calves

Average mortality rate (%)

No. of households with local calves

No. of dead calves


Lushoto 60 0 0.0 7 0 0.0

Mvomero 23 3 5.8 127 320 8.1

Handeni 3 0 0.0 200 134 6.5

Kilosa 7 0 0.0 89 36 2.3

Total 93 3 1.4 423 490 6.0

Of the 3 dead exotic calves reported in Mvomero, 2 died due to disease translating to a

mortality rate of 3.6% (Table 50). Households with local calves in Handeni had the highest

mortality rate (5.0%) attributed to disease (Table 50).

Table 50: Calf mortality rates due to disease

District

Exotic calves Local calves

N (households exotic with

calves)

No. of dead calves


N (households with local

calves

No. of dead calves


Lushoto 60 0 0.0 7 0 0.0

Mvomero 23 2 3.6 127 62 4.0

Handeni 3 0 0.0 200 82 4.9

Kilosa 7 0 0.0 89 19 1.7

Total 93 2 0.9 423 163 3.9

50

CONCLUSION

Overall animal performance was quite low and this is associated with both genetic and

environmental factors. Achieving maximum genetic potential means concerted efforts in

improving the indigenous herd through crossing with improved breeds. Meanwhile,

performance of indigenous herds could be improved significantly through better health care

and access to sufficient feed and water.

Adoption of fodder improvement technologies was low with farmers depending on natural

pastures, especially in extensive areas of Mvomero, Handeni and Kilosa. Shortage of

pastures, other fodder types and water was experienced in the dry months. Drought ranked

second after diseases in contributing towards cattle mortality.

Another limitation to improved performance was farmer access to inputs and services. Despite

reporting availability of animal health services, farmers often administered the health-related

services on their own with or without professional assistance. Access to animal health service

providers can be facilitated through credit facilities. There is need therefore to establish

and/or strengthen links with financial service providers in the four districts.

To improve milk marketing and obtain better prices, farmers could be supported to form

common interest-based groups as a basis for collective action. With a platform to promote the

uptake of new technologies, the smallholder farmer would likely achieve a substantial

transformation of their dairy enterprises.

the moremilkit project: baseline...

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