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Technical efficiency and technological gaps among smallholder
beef farms in Botswana: A stochastic meta-frontier approach
...
Agriculture in Botswana:
 The main source of income and employment in Rural
areas (42.6 percent of the total population)
...
3,060
1,788
2,247
0
500
1000
1500
2000
2500
3000
3500
'000
Commercial
Traditional
 Dualistic structure of production, wit...
Background
(Cont.)
 Despite the numerical dominance , productivity is low esp. in
the communal/traditional sector
3
0
0.0...
 Growing domestic beef demand and on-going
shortage of beef for export:
 In recent years beef export has been declining
...
 To measure farm-specific TE in different farm types
and analyze the determinants of farmers’ TE
 To measure technology-...
Measuring efficiency
Measuring efficiency: potential input reduction or potential output
increase relative to a reference ...
Literature review (Cont..)
Source: Adapted from Battese et al. (2004).
Figure 1: Metafrontier illustration
7
• Household data, collected by survey
• More than 600 observations (for this study classified by farm types)
Data and Meth...
SFA
Reject
hypothesis
Stop
Accept hypothesis
Linear
programming/Shazam
LR test
TE effects/TobitTechnology Gaps
Bootstrapin...
Results and discussion
Production function estimates
Variable
Pooled Stochastic
frontier Metafrontier
Constant (β0 ) 10.6*...
35%
46%
57%
50%
46%
84%
81%
76%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Cattle farms Cattle and
crop farms
Mixed farms Tota...
Technical efficiency
 Beef herd size
 Non farm Income
 HH- age
 Sales to BMC
 Controlled
breeding method
 Other agri...
- The majority of farmers use available technology
sub-optimally and produce far less than the
potential output; average M...
Conclusion and policy implications
14
- It is important to provide relevant livestock extension
and other support services...
- Access to market services, including contract
opportunities with BMC.
- Provide appropriate training/education services
...
The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is given ...
Metafrontier
This technique is preferred in the present study because :
- Enables estimation of technology gaps for differ...
SFA Tobit
Variables Coefficient St Dev Coefficient St Dev
Constant (β0) 3.71*** 0.149 0.41*** 0.030
Beef herd size (δ1) -0...
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Technical efficiency and technological gaps among smallholder beef farms in Botswana: A stochastic meta-frontier approach

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Presented by Sirak Bahta (ILRI) at the Conference on Policies for Competitive Smallholder Livestock Production, Gaborone, Botswana, 4-6 March 2015

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Technical efficiency and technological gaps among smallholder beef farms in Botswana: A stochastic meta-frontier approach

  1. 1. Technical efficiency and technological gaps among smallholder beef farms in Botswana: A stochastic meta-frontier approach Sirak Bahta (ILRI) Conference on Policies for Competitive Smallholder Livestock Production Gaborone, Botswana, 4-6 March 2015
  2. 2. Agriculture in Botswana:  The main source of income and employment in Rural areas (42.6 percent of the total population)  30 percent of the country’s employment  More than 80 percent of the sector’s GDP is from livestock production  Cattle production is the only source of agricultural exports Background 1
  3. 3. 3,060 1,788 2,247 0 500 1000 1500 2000 2500 3000 3500 '000 Commercial Traditional  Dualistic structure of production, with communal dominating Background (Cont.) Cattle population 2
  4. 4. Background (Cont.)  Despite the numerical dominance , productivity is low esp. in the communal/traditional sector 3 0 0.03 0.06 0.09 0.12 0.15 0.18 Sales Home Slaughter Deaths GivenAway Losses Eradication Commercial Traditional
  5. 5.  Growing domestic beef demand and on-going shortage of beef for export:  In recent years beef export has been declining sharply (e.g. from 86 percent of beef export quota in 2001 to 34 percent in 2007 (IFPRI, 2013 )) Background (Cont.) 4 0 30000 60000 90000 120000 150000 180000 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Quantity (tonnes) Value (1000 $)
  6. 6.  To measure farm-specific TE in different farm types and analyze the determinants of farmers’ TE  To measure technology-related variations in TE between different farm types  To Come up with policy recommendations to improve competitiveness of beef production Objective of the study 5
  7. 7. Measuring efficiency Measuring efficiency: potential input reduction or potential output increase relative to a reference (Latruffe, 2010). Technological differences • Comparison of farms operating with similar technologies. • However, farms in different environments (e.g., production systems) do not always have access to the same technology. Assuming similar technologies = erroneous measurement of efficiency by mixing technological differences with technology-specific inefficiency. • Meta-frontier Enables estimation of technology gaps for different groups It captures the highest output attainable, given input (x) and common technology. 6
  8. 8. Literature review (Cont..) Source: Adapted from Battese et al. (2004). Figure 1: Metafrontier illustration 7
  9. 9. • Household data, collected by survey • More than 600 observations (for this study classified by farm types) Data and Methodological Approach Study Area 8
  10. 10. SFA Reject hypothesis Stop Accept hypothesis Linear programming/Shazam LR test TE effects/TobitTechnology Gaps Bootstraping/ Standard dev. Data and Methodological Approach
  11. 11. Results and discussion Production function estimates Variable Pooled Stochastic frontier Metafrontier Constant (β0 ) 10.6** 7.46*** 0.141 0.000010 Feed Equivalents(β1 ) 0.10** 0.20*** 0.058 0.00001 Veterinary costs(β2 ) 0.40*** 0.21*** 0.123 0.0001 Divisia index (β3 ) 0.30** 0.50*** 0.1005 0.00029 Labour (β4 ) 0.10 0.10*** 0.0977 0.0001 σ2 0.45*** 0.03 N 568 568 ϒ 0.99*** Log likelihood -518.63 456.66 Table1: Production function estimates 10
  12. 12. 35% 46% 57% 50% 46% 84% 81% 76% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Cattle farms Cattle and crop farms Mixed farms Total TE w.r.t. the meta-frontier Meta- technology ratio Percent Results and discussion Technology ratio and TE wrt to meta frontier Technical efficiency and meta-technology ratios 11
  13. 13. Technical efficiency  Beef herd size  Non farm Income  HH- age  Sales to BMC  Controlled breeding method  Other agric- income  Indigenous breed  Distance to market - Ve + Ve Results Determinants of technical efficiency 12
  14. 14. - The majority of farmers use available technology sub-optimally and produce far less than the potential output; average MTR is 0.756 and TE is 0.496 . - Herd size, Controlled cattle breeding method, access to Agric and non Agric income, market contract (BMC), herd size and farmers’ age all contribute positively to efficiency. - On the contrary, indigenous breed, distance to markets and income and formal education did not have a favorable influence on efficiency. Conclusion and policy implications 13
  15. 15. Conclusion and policy implications 14 - It is important to provide relevant livestock extension and other support services that would facilitate better use of available technology by the majority of farmers who currently produce sub-optimally. - Necessary interventions, for instance, would include improving farmers’ access to appropriate knowledge on cattle feeding methods and alternative feeds. - Provision of relatively better technology (e.g., locally adaptable and affordable cattle breeds and breeding programmes).
  16. 16. - Access to market services, including contract opportunities with BMC. - Provide appropriate training/education services that enhance farmers’ management practices. - Policies that promote diversification of enterprises, including creation of off-farm income opportunities would also contribute to improving efficiency among Botswana beef farmers. Conclusion and policy implications 15
  17. 17. The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is given to ILRI. better lives through livestock ilri.org Ke a leboga!! Thank you !!
  18. 18. Metafrontier This technique is preferred in the present study because : - Enables estimation of technology gaps for different groups - Accommodates both cross-sectional and panel data The stochastic metafrontier estimation involves first fitting individual stochastic frontiers for separate groups and then optimising them jointly through an LP or QP approach. - It captures the highest output attainable, given input (x) and common technology. 7 Measuring efficiency
  19. 19. SFA Tobit Variables Coefficient St Dev Coefficient St Dev Constant (β0) 3.71*** 0.149 0.41*** 0.030 Beef herd size (δ1) -0.031*** 0.0013 0.001*** 0.000 Indigenous breed (δ2) 0.21*** 0.0811 -0.03*** 0.012 Non-farm income (δ3) 0.01*** 0.001 0.002*** 0.0001 Age of farmer (δ4) -0.01** 0.0018 0.001** 0.0003 Gender (% female farmers)(δ5) 0.12 0.0772 0.01 0.0113 Sales to BMC (δ6) -0.16 0.1245 0.04*** 0.0168 Controlled breeding method (δ7) -0.35** 0.1245 0.13*** 0.0159 Distance to commonly used market (Kms)(δ8) 0.01 0.0006 0.002*** 0.0001 Other agricultural income (% of farmers)(δ9) -0.10 0.0671 0.09*** 0.0095 Income-education (δ10) -0.001* 0.00064 Results Ddeterminants of technical efficiency Table2: Determinants of technical efficiency 15

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