Post on 03-Jan-2016
Technical efficiency and technological gaps among smallholder beef farms in Botswana: a
stochastic meta-frontier approachPOLICIES FOR COMPETETIVE SMALLHOLDER LIVESTOCK PRODUCTION
4-6 MARCH 2015, GABORONE, BOTSWANASirak Bahta
International Livestock Research Institute (ILRI)
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
0
500
1000
1500
2000
2500
3000
35003,060
1,788
2,247
'000
Commercial
Traditional
Dualistic structure of production, with communal dominating
Background(Cont.)
Cattle population2
Background(Cont.)
Despite the numerical dominance , productivity is low esp. in the communal/traditional sector
3
Sales
Home Slaughter
Deaths
GivenAway
Losses
Eradication
0
0.03
0.06
0.09
0.12
0.15
0.18
CommercialTraditional
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.)
42000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
0
30000
60000
90000
120000
150000
180000
Quantity (tonnes)
Value (1000 $)
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
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Measuring efficiencyMeasuring 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-frontierEnables estimation of technology gaps for different groupsIt captures the highest output attainable, given input (x) and common technology.
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Literature review (Cont..)
Source: Adapted from Battese et al. (2004).
Figure 1: Metafrontier illustration
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• Household data, collected by survey• More than 600 observations (for this study classified by farm types)
Data and Methodological ApproachStudy Area
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SFA
Reject hypothesis
Stop
Accept hypothesis
Linear programming/Shazam
LR test
TE effects/TobitTechnology GapsBootstraping/ Standard dev.
Data and Methodological Approach
Results and discussionProduction function estimates
VariablePooled 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 568ϒ 0.99***Log likelihood -518.63 456.66
Table1: Production function estimates
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Cattle farms Cattle and crop farms
Mixed farms Total0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
35%
46%
57%
50%46%
84%81%
76%
TE w.r.t. the meta-frontier
Meta-technology ratioPe
r cen
t
Results and discussionTechnology ratio and TE wrt to meta frontier
Technical efficiency and meta-technology ratios
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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
ResultsDeterminants of technical efficiency
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- 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
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Conclusion and policy implications
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- 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).
- 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
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Ke a leboga!!Thank you !!
MetafrontierThis technique is preferred in the present study because :- Enables estimation of technology gaps for different
groups- Accommodates both cross-sectional and panel dataThe 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.
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Measuring efficiency
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*** 0.0159Distance to commonly used market (Kms)(δ8) 0.01 0.0006 0.002*** 0.0001Other agricultural income (% of farmers)(δ9) -0.10 0.0671 0.09*** 0.0095
Income-education (δ10) -0.001* 0.00064
ResultsDdeterminants of technical efficiency
Table2: Determinants of technical efficiency
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