Livestock Research for Rural Development 25 (11) 2013 Guide for preparation of papers LRRD Newsletter

Citation of this paper

Constraints, livelihoods and technology adoption of village chicken producers in Uganda

W N Nanyeenya1, A Mugisha2, S P Musinguzi3, R Magambo4 and M Senoga5

1 National Livestock Resources Research Institute, PO Box 96, Tororo, Uganda
willinany@gmail.com
2 Makerere - University, PO Box 7062, Kampala, Uganda
3 Rukungiri District Local Government (DLG)
4 Nkoola Institutional Development Associates (NIDA) Po Box 22130, Kampala, Uganda
5 Innovations for Sustainable Economic and Technical Transformation (INSETT) Kampala, Uganda

Abstract

The study was conceived on realization that livestock farmers face numerous constraints despite existence of appropriate management technologies and local markets. Traditional backyard systems of low technology adoption compromise maximum benefits from the enterprises. The objectives of the study were to: determine adoption factors, assess experiences, constraints and opportunities for technology packaging and dissemination, and establish prospects for sustained and accelerated livestock technology uptake. The study was conducted using both qualitative and quantitative survey techniques based on Community Group Discussions, and 75 households selected using multi-stage and systematic proportion to size random sampling procedure, respectively. Data were analysed using descriptive statistics and logistic regression econometric modeling.

Most (98 per cent) households kept indigenous chickens of mean flock size of 13 that directly contributed to about 10 per cent of overall farm income through sales of eggs and live birds. The enterprise is a hub for several cultural, nutritional and social capital household community needs. There is gender neutrality in ownership, management access to and control of benefits from chickens. The major constraints are diseases - precipitated and signified by lack of vaccines, high chick mortality, New Castle Disease (NCD) regular outbreaks; poor feeding and housing due to lack of knowledge on husbandry. T-test and chi square results on adoption of vaccination technologies show that vaccinators raised bigger flocks, belonged to farmer groups, were closer to weekly live bird markets, were wealthier in terms of cattle assets and land size and had more family labour compared to non-adopters. Logistic regression results show that market access, membership to farmers’ groups, wealth (cattle ownership) and flock size (tendency to commercialization) are the prime drivers of adoption of vaccinations among chicken farmers. It can be concluded that disease management especially through establishing a farmer-led vaccination system, enhanced flock and chick management through improved structures and feed supplementation are critical intervention areas for boosting chicken productivity. These findings suggest that access to live bird markets, and input supplies, combined input procurement, lack of a cold chain, availability of disposable income, and tendency to commercialization are key determinants of adoption of chicken vaccinations technology. It is recommended that village level chicken producer associations be strengthened to ease vaccine bulk purchase and management of vaccine cold chain. In addition producer exposure improved feeding, general health and housing technologies is necessary to further enhance chicken profitability.

Key words: community, distribution, farmer-managed, vaccination


Introduction

Livestock plays important household livelihood enhancing roles by enabling of savings, providing security (insurance) for emergencies, making informal convertible savings, asset accumulation, finance planned expenditures, maintaining social capital (cultural values) and providing livestock products (Ashley and Nanyeenya 2005). Livestock farmers face yield and resource constraints and risks like variable weather and environmental conditions, animal diseases, pests, parasites and accidents; insufficient feeds and uncertain inputs ( Lien and Hardaker 2001). Innovative and rational management of relevant breeds, feeding, housing and health technologies are hence vital for increased farm productivity both in resource endowed and disadvantaged circumstances. Appropriate breeds, feeding, housing and health technologies for increased productivity exist (NARO-LSRP 1999; Farm Africa 2006), but traditional livestock systems as a result of low adoption of such technologies dominate farm production leading to failure to get maximum farm benefits from farm enterprises (Nanyeenya et al 2008) .

Objectives of the Study

The major objective of the study was to establish favourable and disabling factors for technology adoption with a view to enhancing technology packaging, promotion and dissemination.

The Specific Objectives were to:


Materials and Methods

Study Area and Data Collection

The study was conducted in Kibuku district in Eastern Uganda. The district borders the districts of Budaka in the east, Kumi in the north, Kamuli in the west, Tororo and Iganga in the south and Soroti in the north-west. Agriculture is the main economic activity with major crops being millet, potatoes, beans, bananas, simsim and sunflower and cotton. These crops are integrated with small holder cattle of about three head, goats, chickens and turkeys. This area is traversed by the Kampala – Mbale – Soroti highway and located between three district towns namely Budaka, Pallisa, and Mbale. These towns and local live bird markets in Tirinyi, Pallisa, Kadama and Kibuku – located in a radius of not more than ten kilometers offer local demand for live birds and eggs.. Both formal and informal data capture methods were use in the study. Data were collected by direct interviews using standard questionnaires for 75 households. This was supplemented by Community Group Discussions (CGDs).

Sampling Techniques

Households were selected using multi-stage and systematic proportion to size random sampling procedure. The following were stratifying criteria: Relative importance of enterprise in the area, proportion of farmers managing enterprise, status of past technology interventions by the National Agriculture Research Organisation (NARO) and priority ranking of enterprise by the National Agriculture Advisory Services (NAADS), proximity of farms to markets, degree of commercialization, and degree of extension and advisory efforts.

Data Analysis

Non-statistical and statistical methods of data analysis were used. For qualitative data the key issues examined were: role of the enterprise, gender in management and major Constraints. Data were synthesized using trend, content and context analysis, pair-wise ranking and profiling techniques. Vaccination against major diseases such as New Castle Disease (NCD), Gumboro and fowl typhoid is a key management technology for improving chicken productivity. During exploratory surveys the role of each enterprise in the livelihood patterns of the producer communities, participation of the women, men and children in daily and routine chicken activities as well as access to and control of household resources and benefits were examined. In addition, major farm constraints and coping mechanisms were examined using pair-wise ranking. Effective dissemination of crop and livestock technologies requires a sound understanding of users’ agricultural technology sources, adoption levels and causes of the status of rates and levels of adoption. Conroy et al (2005) noted that many projects rely solely on conventional government extension services to disseminate information using standard and uniform procedures yet farmers are of diverse typologies given their resource, market access, risks and technology exposure.

In the study area, there is limited exposure to, and in many cases and total non-existence of a chicken vaccine supply systems. Many farmers commonly raise their birds without vaccination. Technology adoption was assessed on whether or not a household regularly vaccinates. .Variations in characteristics of adopters and non-adopters were examined using descriptive statistics such as means, frequencies, chi squares, t-tests. In order to establish determinants of adoption of vaccination technology, a logistic regression model was fitted as follows:

Logistic Regression Theoretical Model

In logistic regression models a dependent variable is an indicator of a characteristic being true or not; or an event taking place or not and therefore takes on dummy value of 1 if the characteristic is valid or event takes place; and 0 otherwise. The probability of an event is given by the chance that it occurs or not. According to Studenmund 1991, if the probability for event to occur is p , then 1–p is the probability for the event not occurring. The odds ratio for the event to occur can be specified as:

 

The logistic function is transformed to linearity by taking natural logs to give a log of odds (the ‘Logit’), which is specified as:

 

Where:

1n is the natural logarithm

Logistic Regression Empirical Model

In this study, free range chickens are largely local breeds with limited confinement, poor night housing and feed supplementation. They are prone to several diseases but if vaccinated, this risk is tremendously reduced, and productivity and flock expansion improved. Technology adoption was examined by considering the households that regularly vaccinate (adopters) and those whose flock depend on natural survival of the fittest without protection through vaccination (non-adopters). Empirically logistic regression was used to determine factors affecting adoption of free range chicken vaccination. In the logistic regression (Greene 2000), the focus is on probability of practicing vaccination (Y) was weighed against household labour, farmer skills/experience and land resources, gender, location, degree of commercialisation and market access ( Xi). The logistic regression empirical equation was specified as:

 Where

Y = Dummy dependent variable with 1 = Regular vaccination, 0 otherwise

Sexhh = Sex of household head (1 = female, 0 otherwise)

Expr = Years of farming experience of household head

Educ = Years of formal education of household head

Lnd = Household land holding in acres

Membr = Membership to farmer groups

HHsze = Total number of members in the household

Floksz = Total number of chickens (flock)/degree of commercilisation

PRED = Risk of predators on the farm

Mkt = Distance to local markets in kilometres

Vetsev = Access to regular veterinary services

CatNo = Cattle herd size owned by the household

α= Intercept

β = Coefficient on the independent variables

ε = the error term following a normally distributed function


Results and Discussion

Free Range Chickens, Livelihood Patterns and Gender

In the study area, indigenous chickens rank highly (third after goats and crops) in terms of contribution to the farmers’ livelihoods (Figure 1). The chickens readily fetch cash income and yield quick returns to investment through sales of eggs and live birds and contribute to household cash and nutritional security of all household members through consumption of eggs and meat as well as playing cultural roles. Crop production (67 %) is the main source of household income. The contribution of chickens (7 %) is comparable to that of goats (9 %) although off takes of the former are much higher. Wealth accumulation by transferring from small stock to large ruminants starts by exchange five birds usually one cock, three laying hens and one pullet) for one goat. Seven mature goats are then exchanged for a local heifer or bullock. Though uncommon, chickens can directly be exchanged for one head of cattle. Conversely, farmers use other livestock to get or expand the chicken flocks by surrendering one goat for five birds.

I once exchanged one hundred three birds of which twelve were cocks for one bullock, revealed one chicken keeper

Figure 1: Main sources of household income

 Chickens are therefore the kick-start capital for boosting wealth status of households. Similar to what was observed by Ngugi et al 2002, at the community level chickens are used in entertaining visitors in households and during ceremonies such as weddings, initiation of twins, funerals and bride price settlements and negotiations. Chickens are the gifts an intending husband gives to in-laws and parents of the girl on the first visits to a girls’ home. Chickens are used in contribution to healthcare of households. They are used as in-kind payment especially for medical bills in case the patient or care - takers do not have cash. Chickens are also presented to traditional healers (doctors) to appease spirits and cure the diseases. Chickens are integrated in settlement (in-kind payment) for hired labour. Chickens are used in social networks and pooling money during fund raising functions. They are exchanged to get items such as clothes. There is gender neutrality in ownership of chickens. Donations of chicken as start up stocks are common to children by their parents or grandparents. All household members participate in making nests, taking care of brooding hens, supplementary feeding and control of parasites and diseases. Chickens can be reared by all household age and sex groups in household thus enhancing gender neutrality for ease of entry into livestock asset accumulation. Chickens are traditionally reared under free range systems in which birds scavenge for feeds around the homestead and neighboring fields. This management system is low cost due to limited confinement and use of purchased inputs. A modified form, the semi-intensive system, is associated deliberate feed supplementation and eases other management practices like deworming, vaccinations and disease control.

Production Constraints

Using both CGDs and formal surveys, main constraints encountered by free range chicken producers were established and are presented in table 1 and Figure 2. Similar to Nanyeenya et al (2009) disease was the biggest management problem in chickens.

Figure 2: Constraints to chicken production

Table 1:  Pair –wise ranking of main constraints of free range chickens

 

Chick

mortality

(CM)

Thieves

(THV)

Predation

(PRED)

Worms

(WMS)

Lack of vaccines

(VAC)

Poor feed supplements

(INGR)

 

Mites

(MTS)

Poor housing

(HSG)

NCD

Low

market

prices

(LMP)

Chick mortality

 

CM

CM

CM

VACC

CM

CM

CM

CM

CM

Thieves

 

 

PRED

THV

VACC

INGR

THV

HSG

NCD

LMP

Predation

 

 

 

PRED

VACC

INGR

PRED

HSG

NCD

LMP

Worms

 

 

 

 

VACC

INGR

WMS

HSG

NCD

LMP

Lack of vaccines

 

 

 

 

 

VACC

VACC

VACC

VACC

VACC

Poor feed supplementation

 

 

 

 

 

 

INGR

INGR

NCD

INGR

Mites

 

 

 

 

 

 

 

HSG

NCD

LMP

Housing

 

 

 

 

 

 

 

 

NCD

HSG

NCD

 

 

 

 

 

 

 

 

 

NCD

Low  market prices

 

 

 

 

 

 

 

 

 

 

Total

8

2

3

1

9

6

0

5

7

4

Rank

2

8

7

9

1

4

10

5

3

6


Other major constraints (Table 1) include difficulty in procuring vaccines, high chick mortalities, New Castle Disease (NCD and poor feed supplementation. The pair-wise ranking of constraints shows that there are problems associated with getting vaccines and maintaining a cold chain. Vaccines are obtained from centres located more than 15 kilometers from the farms. This further compounds the disease problem making chick mortality and New Castle Disease second and third most important constraints, respectively. Other diseases cited were gumboro, fowl typhoid, coccidiosis and Infectious bronchitis. Finally, lack of inputs and skills for mixing chicken rations, poor housing conditions and low market price were mentioned as important constraints in that order.

Characteristics of adopters and non-adopters of chicken vaccination

Variations in characteristics of vaccinators and non-vaccinators are shown in table 2 and figure 3. Most (50 per cent) of the farmers had attained elementary education as the highest level. Packaging of technology must be done in a simple manner such that it can be appreciated. Findings in table 2 indicate that 48 % of the farmers vaccinated their birds. Vaccinators raised higher flocks (ρ = 0.001), were closer to bird markets (ρ = 0.002), were wealthier in terms cattle assets and land size (ρ = 0.002 and ρ = 0.075, respectively), belonged to farmer groups (ρ = 0.073) and had more family labour (ρ = 0.046) compared to non-adopters. There was no relationship between sex of household head and adoption of vaccination. These results suggest that chicken market access, accessibility to supply, combined procurement and cold chain management, income levels, and tendency to commercialization are prime drivers of adoption of vaccinations. Results on household means of transport indicated that most farmers used bicycles (78 %) as the basic transport means for various household needs. This form of transport may not be sufficient to get vaccines from urban areas located more than 10 kilometres from the production areas.


Figure 3:
Education levels of vaccination adopters and non-adopters (Per cent)

Table 2:  Characteristics of adopters and non-adopters of chicken vaccinations

 

Adopters

Non-adopters

 

 

Mean

SD

Meam

SD

p

 

Distance to nearest weekly market, km

1.58

 

1.55

 

2.92

 

2.31

 

 

0.002

 

Experience chicken management, yrs

24.12

 

12.04

 

22.51

 

9.68

 

0.102

Age of household head, yrs

42.06

 

15.73

 

39.03

 

14.50

 

0.977

Household size

9.0

 

7.0

 

7.0

 

4.0

 

0.046

Total land size, acres

6.76

 

13.36

 

3.64

 

2.80

 

0.075

Cattle herd,

4.0

 

4.0

 

2.0

 

2.0

 

0.002

Chicken flock

32.0

 

35.0

 

15.0

 

16.0

 

0.001

Distance to main town, km

13.07

 

8.85

 

16.35

 

8.15

 

1.440

 

 

Number

Percentage

Number

Percentage

 

Sex of household head

Male

Female

30

4

88.2

5.6

29

8

78.4

11.3

 

 

0.268

Membership to farmers’ groups

Yes

No

8

26

23.5

76.5

3

34

8.1

91.9

 

 

0.073


Factors affecting adoption of chicken vaccination

Similar to findings of Jera and Ajayi 2008, adoption of chicken vaccination was affected by membership to farmers’ groups (ρ = 0.037), access to live bird markets (ρ = 0.034), chicken flock size (ρ = 0.060), and cattle herd size (ρ = 0.012). These results suggest that awareness, skills improvement, joint effort and ability to pool funds that are commonly attributed to farmer groups or associations had a significant and positive effect on the adoption of chicken vaccination technology. Access to product markets ensures sustained demand for farmers. This fact was confirmed by the results of this study. The closer the farmers were to the live bird markets the higher were the chances of adoption of vaccination practices. The probability of adopting the technology would increase by a factor of 4.5 if the farm was closer to the markets by 1 kilometer compared to non-adopters. The bigger the chicken flock size became the more the likelihood of adoption of chicken vaccination increased. Larger flocks imply tendency for commercialization. As farmers get more commercialized their propensity to adopt vaccination technology increases. Cattle asset structure significantly and positively affected adoption of chicken vaccination. .Cattle ownership is a proxy for wealth and income stability in rural livelihoods. This implies that the wealthier and revenue endowed households had higher chances of adopting chicken vaccination technology (See table 3).

Table 3: Factors affecting adoption of chicken vaccination

Variable

B

Wald

Sig.

Exp(B)

Membership to farmer Groups (Yes or No)

3.162

4.332

0.037

23.626

Access to live bird markets (Kilometers)

-0.455

4.498

0.034

0.635

Experience to chicken rearing (years)

-0.050

1.337

0.248

0.951

Sex of household head (Yes or No)

0.012

0.001

0.993

0.988

Level of education of household head

0.248

0.156

0.693

1.282

Total  household size (Number)

-0.061

0.346

0.556

0.941

Total land size (Acres)

-0.108

1.129

0.288

0.897

Chicken Flock size (Number)

0.032

3.524

0.060

1.032

Risk of predators (Yes or No)

22.358

0.001

0.998

0.000513

Access to veterinary services (Yes or No)

0.784

0.585

0.444

2.190

Cattle herd size (Number)

0.663

6.259

0.012

1.940

Constant

-22.241

0.001

0.998

0.001

Goodness of Fit Parameters

Values

Model

0.001

Maximum likelihood

47.3

Overall cases predicted

85.7%

Correctly predicted adopters

85.3%

Correctly predicted non-adopters

86.1%

Sample size

71


Conclusions


Acknowledgements

The funding for this research was though ATAAS/IDA/World Bank and Government of Uganda. The management of NARO - NaLIRRI, Collaborators, farmers, local administrators and extension workers in Kibuku district are appreciated.


References

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Received 4 April 2013; Accepted 28 September 2013; Published 1 November 2013

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