Livestock indicators for targeted investments:           Translating constraints into opportunities in Tanzania           ...
TOPICS OF DISCUSSION   • Context   • Conceptual issues   • Approaches and methods   • Data issues   • Econometric analysis...
LDIP’s THREE MAJOR COMPONENTS   • Component 1: data collection and analysis      1.1 - assessing the role of livestock in...
WHAT IS A CONSTRAINT?•      The theory of constraints (TOC) states that ‘a chain is no stronger       than its weakest lin...
TYPES OF CONSTRAINTS   •      Constraints occur in many different forms   •      However, binding constraints in most syst...
APPROACHES AND METHODS... (1)   •      Descriptive methods to collated information through desk          reviews   •      ...
TWO STAGE DEA • Measure efficiency of each farm in the sample       (0 < eff ≤ 1) • Explain efficiency/inefficiency in ter...
WHICH LIVESTOCK PRODUCT?•      Milk was selected as a suitable livestock product for       constraint analysis in the cont...
Market opportunity in Tanzania                             % change in consumption of animal foods                        ...
ACTIVITY TIMESCALES   •      Tight timescale for this subcomponent - constraint analysis          comes at the end of the ...
SEQUENCE ACTIVITIES   •     Stage 1: Use Tanzanian LSMS data 2008 and conduct         preliminary constraint analysis usin...
TANZANIA 2008 - MILK PRODUCERS (%)  Region             N        Milk          Region           N       Milk               ...
MILK PRODUCTIVITY(Liters /cow/day)…(1)                        N          mean       median Std. Dev. Min                  ...
MILK PRODUCTIVITY (Liters /cow/day)…(2)                             N        mean median Std. Dev. Min                    ...
DETERMINANTS OF MILK YIELD• Evaluation of factors influencing productivity uses an approach  similar to that used by Birth...
DETERMINANTS - SUMMARY STATISTICS (n=259)                                        Mean median Std. Dev. Min MaxFarmer keeps...
MODEL RESULTS…. (1)                                                                        Coef.       Std. Err. P>tConsta...
MODEL RESULTS ….(2)                                                             Coef.          Std. Err. P>t   Access to m...
SETS OF CONSTRAINTS/OPPORTUNITIES?• Resource constraints (e.g. family size+, farm       size?, biophysical environment?)• ...
FURTHER ACTIVITIES  • Stakeholders’ workshop to identify and rank constraints to dairy production      Tanzania and Uganda...
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Livestock indicators for targeted investments: Translating constraints into opportunities in Tanzania

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Presented by Ayele Gelan and Francis Wanyoike at the Smallholder Dairy Value Chain in Tanzania Stakeholder Meeting, Morogoro, Tanzania, 9 March 2012

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

  1. 1. 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 2012Joint project of the World Bank, FAO, AU-IBAR, ILRI with support from the Gates Foundation
  2. 2. TOPICS OF DISCUSSION • Context • Conceptual issues • Approaches and methods • Data issues • Econometric analysis • Summary and next stepsLivestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  3. 3. 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 managementLivestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  4. 4. 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’Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  5. 5. 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”)Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  6. 6. 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 analysisLivestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  7. 7. 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 => constraintsLivestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  8. 8. 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]Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  9. 9. 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 PorkLivestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  10. 10. 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)Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  11. 11. 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 2Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  12. 12. 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 13.8Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  13. 13. 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 3.0Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  14. 14. MILK PRODUCTIVITY (Liters /cow/day)…(2) N mean median Std. Dev. Min MaxTabora 19 1.2 0.8 2.2 0.1 10.0Rukwa 4 1.4 0.8 1.5 0.5 3.6Kigoma 2 0.4 0.4 0.3 0.2 0.7Shinyanga 42 1.6 0.7 2.6 0.2 12.0Kagera 5 0.4 0.5 0.2 0.2 0.6Mwanza 14 1.1 0.5 1.8 0.2 7.2Mara 9 2.2 1.5 2.5 0.3 7.5Manyara 35 1.4 0.8 2.0 0.3 10.0Kaskazini Unguja 3 2.3 1.7 1.1 1.7 3.6Kusini Unguja 2 0.6 0.6 0.1 0.5 0.7Mjini Unguja 5 1.6 2.3 1.2 0.1 2.5Kaskazini Pemba 6 2.1 1.8 1.5 1.0 5.0Kusini Pemba 4 1.8 1.0 2.2 0.3 5.0Tanzania 289 1.9 1.0 2.5 0.1 15.0Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  15. 15. 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)Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  16. 16. DETERMINANTS - SUMMARY STATISTICS (n=259) Mean median Std. Dev. Min MaxFarmer keeps improved dairy breed (0,1) 0.1 0.0 0.4 0.0 1.0Size of household (count) 6.9 6.0 3.4 1.0 26.0Number of family farm workers 2.7 2.0 1.8 0 12Level of education of HHH (scale of 0 to 5) 0.3 0.0 0.7 0.0 3.0Land size (acres) 8.4 4.3 13.1 0.3 118.0Number of TLU’s of livestock in the farm 9.6 6.2 10.3 1.0 71.5Extension from an NGO (0,1) 0.0 0.0 0.1 0.0 1.0Extension from a large scale farmer (0,1) 0.0 0.0 0.1 0.0 1.0Mainly sells milk to a local merchant 0.1 0.0 0.2 0.0 1.0Milk quantity of sold (L/yr) 324.9 0.0 1,089.3 0.0 13,680.0Length of growing period(scale of 1 to 3) 2.1 2.0 0.8 1.0 3.0Access to market (scale of 1 to 3) 2.4 3.0 0.8 1.0 3.0Population density (scale of 1 to 3) 2.3 2.0 0.6 1.0 3.0Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  17. 17. MODEL RESULTS…. (1) Coef. Std. Err. P>tConstant* -0.84 0.49 0.09Farmer keeps improved dairy breed (0,1)** 0.39 0.19 0.04Log size of household** 0.31 0.13 0.02Number of family farm workers -0.23 0.16 0.14Level of education of HHH (scale of 0 to 5) 0.09 0.08 0.27Log land size (acres) 0.07 0.06 0.28Log total number of TLU of livestock in the farm*** -0.50 0.07 0.00Extension information from an NGO (0,1) 0.46 0.43 0.28Extension information from a large-scale farmer(0,1) 0.34 0.38 0.37Mainly sells milk to a local merchant(0,1) -0.37 0.29 0.20Log Quantity of milk sold (Litres /yr)*** 0.08 0.02 0.00 Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  18. 18. 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 densityLivestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  19. 19. 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+)Livestock Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org
  20. 20. 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 Data Innovation in Africa Numbers for Livelihood Enhancement www.africalivestockdata.org

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