Livestock indicators for targeted investments: Translating constraints into opportunities in Tanzania

Livestock indicators for targeted investments:
Translating constraints into opportunities in Tanzania

Ayele Gelan and Francis Wanyoike
International Livestock Research Institute

The Smallholder Dairy Value Chain in Tanzania Stakeholder
Meeting , Morogoro, Tanzania, 9 March 2012

Joint project of the World Bank, FAO, AU-IBAR, ILRI with support from the Gates Foundation

TOPICS OF DISCUSSION

• Context
• Conceptual issues
• Approaches and methods
• Data issues
• Econometric analysis
• Summary and next steps

Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org

LDIP’s THREE MAJOR COMPONENTS

• Component 1: data collection and analysis
 1.1 - assessing the role of livestock in poverty
reduction
 1.2 - identifying livestock product ‘hot spots’ and
creating opportunities for market participation by
smallholder livestock keepers
 1.3 - increasing income through constraint
analysis
• Component 2: advocacy and communication
• Component 3: project activity coordination and
management

WHAT IS A CONSTRAINT?

• The theory of constraints (TOC) states that ‘a chain is no stronger
than its weakest link’
• However, TOC is narrowly focused on contexts of modern
business management, which is different from the nature of
constraints in small holder farming systems
• We have adapted the TOC approach more broadly to address
constraint analysis in the context of this study
• In the context of smallholder livestock production systems,
therefore, a working definition of a constraint can be ‘any barrier
that prevents livestock keepers from achieving their goal to
improve their livelihoods’


TYPES OF CONSTRAINTS

• Constraints occur in many different forms
• However, binding constraints in most systems are
often very few in number
• They range from bio-physical, resource and technical
constraints to those associated with socio-cultural
factors, infrastructure and policy
• An important attribute of constraints is that they are
not easily observed, and as a result are often
confused with their symptoms (such as “low
productivity”)


APPROACHES AND METHODS... (1)

• Descriptive methods to collated information through desk
reviews
• Participatory rural appraisal, which involves active
participation of farmers to identify constraints and plan
appropriate solutions
• Linear programming has often been applied to identify binding
constraints from a known list
• Econometric methods to estimate agricultural supply
responses
• Data envelopment analysis (DEA) that combines farm
efficiency analysis


TWO STAGE DEA

• Measure efficiency of each farm in the sample
(0 < eff ≤ 1)
• Explain efficiency/inefficiency in terms of socio-
economic, and biophysical conditions
• Positive coefficients => opportunities
• Negative coefficients => constraints

WHICH LIVESTOCK PRODUCT?

• Milk was selected as a suitable livestock product for
constraint analysis in the context of this project
• Demand analysis (component 1.2 of this project)
showed that milk consumption is expected to grow fast
in Tanzania
• Latest LSMS data was made available for Tanzania
(2008) [now perhaps we can consider using sample
census data]

Market opportunity in Tanzania

% change in consumption of animal foods
in response to % change in income
1

0.8

0.6

0.4

0.2

0
Milk Goat Beef Poultry Eggs Pork


ACTIVITY TIMESCALES

• Tight timescale for this subcomponent - constraint analysis
comes at the end of the project timescale, after suitable data
is collected using the new livestock module built in the LSMS
(The Living Standards Measurement Study)
• The project team discussed and agreed on the importance of
experimenting with the existing Tanzanian LSMS, 2008
• A feasibility of undertaking such preliminary constraint
analysis was conducted during the fourth quarter of 2011
• For a number of reasons, the LSMS 2008 data was not
suitable to conduct the two-stage constraint analysis
(progress report, December 22, 2011)


SEQUENCE ACTIVITIES

• Stage 1: Use Tanzanian LSMS data 2008 and conduct
preliminary constraint analysis using partial productivity
indicators of biophysical relationships
 Measure milk yield (milk per cow per day)
 Explain productivity differences among farms
• Stage 2: Use LSMS 2012 (Tanzania, Uganda) and conduct a
two-stage DEA analysis
 Measure efficiency of farms (Dairy in Tanzania, and Pig in
Uganda)
 Explain efficiency differences among farms in each case
• Qualitative constraint analysis before and validation after
stage 2

TANZANIA 2008 - MILK PRODUCERS (%)
Region N Milk Region N Milk
producers (%) producers (%)
Dodoma 88 8Tabora 104 21
Arusha 79 37Rukwa 83 5
Kilimanjaro 104 42Kigoma 94 2
Tanga 107 20Shinyanga 125 37
Morogoro 99 2Kagera 111 6
Pwani 55 4Mwanza 96 16
Dar es salaam 65 5Mara 45 22
Lindi 145 1Manyara 74 53
Mtwara 184 1Kaskazini Ungunja 63 6
Ruvuma 134 3Kusini Ungunja 25 8
Iringa 123 6Mjini Ungunja 41 15
Mbeya 146 18Kaskazini Pemba 66 9
Singida 48 19Kusini Pemba 72 7
Tanzania 2,376 13.8

MILK PRODUCTIVITY(Liters /cow/day)…(1)
N mean median Std. Dev. Min Max
Dodoma 7 1.9 0.8 2.8 0.3 8.0
Arusha 26 1.7 0.9 2.3 0.1 12.0
Kilimanjaro 38 3.1 2.0 2.6 0.3 12.0
Tanga 18 2.3 1.0 2.8 0.1 9.0
Morogoro 2 8.5 8.5 7.8 3.0 14.0
Pwani 2 0.8 0.8 0.4 0.5 1.0
Dar es salaam 3 9.2 6.7 5.0 6.0 15.0
Lindi 1 1.8 1.8 1.8 1.8
Mtwara 1 1.5 1.5 1.5 1.5
Ruvuma 3 0.7 0.8 0.3 0.3 1.0
Iringa 6 3.2 0.9 4.6 0.3 12.0
Mbeya 23 1.8 1.5 1.2 0.2 5.3
Singida 9 0.9 0.7 0.9 0.2 3.0

MILK PRODUCTIVITY (Liters /cow/day)…(2)
N mean median Std. Dev. Min Max
Tabora 19 1.2 0.8 2.2 0.1 10.0
Rukwa 4 1.4 0.8 1.5 0.5 3.6
Kigoma 2 0.4 0.4 0.3 0.2 0.7
Shinyanga 42 1.6 0.7 2.6 0.2 12.0
Kagera 5 0.4 0.5 0.2 0.2 0.6
Mwanza 14 1.1 0.5 1.8 0.2 7.2
Mara 9 2.2 1.5 2.5 0.3 7.5
Manyara 35 1.4 0.8 2.0 0.3 10.0
Kaskazini Unguja 3 2.3 1.7 1.1 1.7 3.6
Kusini Unguja 2 0.6 0.6 0.1 0.5 0.7
Mjini Unguja 5 1.6 2.3 1.2 0.1 2.5
Kaskazini Pemba 6 2.1 1.8 1.5 1.0 5.0
Kusini Pemba 4 1.8 1.0 2.2 0.3 5.0
Tanzania 289 1.9 1.0 2.5 0.1 15.0

DETERMINANTS OF MILK YIELD

• Evaluation of factors influencing productivity uses an approach
similar to that used by Birthal et al (1999) and Msangi et al (n.d)
• An OLS regression of milk yields against a set of explanatory
variables is conducted
• Milk yields distribution problem - highly skewed!
• As is commonly the case with positively skewed variables (Chen
et al, 2003) the log form of milk yields is more normally
distributed and is used as the dependent variable
• Selection of explanatory variables is guided by literature
including studies by Birthal et al (1999), Msangi et al (n.d) and
Veronique et al (2007)


DETERMINANTS - SUMMARY STATISTICS (n=259)
Mean median Std. Dev. Min Max
Farmer keeps improved dairy breed (0,1) 0.1 0.0 0.4 0.0 1.0
Size of household (count) 6.9 6.0 3.4 1.0 26.0
Number of family farm workers 2.7 2.0 1.8 0 12
Level of education of HHH (scale of 0 to 5) 0.3 0.0 0.7 0.0 3.0
Land size (acres) 8.4 4.3 13.1 0.3 118.0
Number of TLU’s of livestock in the farm 9.6 6.2 10.3 1.0 71.5
Extension from an NGO (0,1) 0.0 0.0 0.1 0.0 1.0
Extension from a large scale farmer (0,1) 0.0 0.0 0.1 0.0 1.0
Mainly sells milk to a local merchant 0.1 0.0 0.2 0.0 1.0
Milk quantity of sold (L/yr) 324.9 0.0 1,089.3 0.0 13,680.0
Length of growing period(scale of 1 to 3) 2.1 2.0 0.8 1.0 3.0
Access to market (scale of 1 to 3) 2.4 3.0 0.8 1.0 3.0
Population density (scale of 1 to 3) 2.3 2.0 0.6 1.0 3.0

MODEL RESULTS…. (1)
Coef. Std. Err. P>t
Constant* -0.84 0.49 0.09
Farmer keeps improved dairy breed (0,1)** 0.39 0.19 0.04
Log size of household** 0.31 0.13 0.02
Number of family farm workers -0.23 0.16 0.14
Level of education of HHH (scale of 0 to 5) 0.09 0.08 0.27
Log land size (acres) 0.07 0.06 0.28
Log total number of TLU of livestock in the farm*** -0.50 0.07 0.00
Extension information from an NGO (0,1) 0.46 0.43 0.28
Extension information from a large-scale farmer(0,1) 0.34 0.38 0.37
Mainly sells milk to a local merchant(0,1) -0.37 0.29 0.20
Log Quantity of milk sold (Litres /yr)*** 0.08 0.02 0.00

MODEL RESULTS ….(2)

Coef. Std. Err. P>t
Access to market*** 0.63 0.21 0.00

Notes: *, **, and *** represent 1%,5%, and 10% levels of statistically significance
L=Low, M=Medium, H=High so LHM = Low LGP, High market access and Medium
population density


SETS OF CONSTRAINTS/OPPORTUNITIES?

• Resource constraints (e.g. family size+, farm
size?, biophysical environment?)
• Infrastructure /policy constraints (e.g., market
access+, existence of milk markets+)
• Within farm constraints (e.g., herd size-; breed
improvement+)


FURTHER ACTIVITIES

• Stakeholders’ workshop to identify and rank constraints to dairy production
Tanzania and Uganda
• Two stage technical efficiency analysis of dairy farms in Tanzania and Pig
farms in Uganda using revised LSMS data (soon after LSMS surveys are
completed)
• Validation of findings from the quantitative farm efficiency analysis through
surveys of selected farms
• Final report on constraint analysis and contributing to advocacy and
communication to inform policies on investments to relax binding constraints.


Livestock indicators for targeted investments: Translating constraints into opportunities in Tanzania

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