Technical efficiency in beef cattle production in Botswana: a stochastic metafrontier approach
Sirak Bahta International Livestock Research Institute (ILRI)
Sirak Bahta
Tropentag 2014: Bridging the gap between increasing knowledge and decreasing resources, Prague, 17−19 September 2014
Outline
Background Objective of the study Literature Review Data and Methodological Approach Results and discussion Conclusion and Policy Implications
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
Cont.Background(Cont.)
Beef is dominant within the Botswana livestock sector
3
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 population4
Background(Cont.)
Despite the numerical dominance , productivity is low esp. in the communal/traditional sector
5
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 ))
Problems in production and marketing into export channels
• High transaction costs• Farmers’ preferences for keeping animals to an
advanced age• Lack of understanding of the various markets’ quality
requirements
Background(Cont.)
6
• To derive a statistical measure of Technical efficiency of different smallholder farm types More specifically:• To measure farm-specific TE in different farm
types• To measure technology-related variations in TE
between different farm types• To analyse the determinants of farmers’ TE• Come up with policy recommendations to
improve competitiveness of beef production
Objective of the study
7
Measuring efficiency: potential input reduction or potential output increase relative to a reference (Latruffe, 2010).
Technically defined by non-parametric and parametric methods
The non-parametric approach uses mathematical programming techniques –Data envelope analysis (DEA)
The parametrical analysis of efficiency uses econometric techniques to estimate a frontier function - Stochastic frontier analysis (SFA)
Literature review (cont..)
8
Literature review (cont..)
Technological differences
The stochastic frontier allows 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 when they actually differ across farms might result in erroneous measurement of efficiency by mixingtechnological differences with technology-specific inefficiency (Tsionas, 2002).
Various alternatives have been proposed to account for differences in technology and production environment.
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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.
Literature review (cont..)
10
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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- Stochastic frontier analysis (Frontier 4.0)
- Linear Programing (SHAZAM)
- Bootstrapping to derive standard deviations of
metafrontiers (SHAZAM)
- Tobit (TE effects)- STATA
Data and Methodological Approach
13
Results and discussionProduction function estimates
VariablePooled Stochastic frontier Metafrontier
Constant (β0 ) 7.04** 7.62***0.188 0.010
Feed Equivalents(β1 ) 0.22** 0.022***0.009 0.001
Veterinary costs(β2 ) 0.106*** 0.75***0.019 0.002
Divisia index (β3 ) 0.091*** 0.003***0.013 0.000
Labour (β4 ) 0.31** 0.008***0.015 0.001
Land(β5 ) 0.291*** 0.315***0.058 0.050
σ2 0.473***0.03
ϒ 0.987***Log likelihood -529.73 440.75
Table1: Production function estimates
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Cattle Farms Cattle & crop farms
Cattle, Samll Stcok & crop
farms
All farms30%
35%
40%
45%
50%
55%
60%
65%
70%
75%
80%
39%
45%
49%
45%
75% 76%79%
77%
TE wrt metafrontiier
Meta-tech-nology ratio
a
aa
cb
b
Per c
ent
Results and discussionTechnology ratio and TE wrt to meta frontier
Table 1: Technical efficiency and meta-technology ratios
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<0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1 10%
10%
20%
30%
40%
50%
60%
70% all sample
catle
cattle-crop
cattle- crop-small stock
Per c
ent
Results and discussionTE wrt to meta frontier distribution
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ResultsDdeterminants of technical efficiency
SFA Tobit
Variables Coefficient St Dev Coefficient St Dev
constant 3.71*** 0.250 0.446 0.030
Herd size -0.031*** 0.001 0.001*** 0.000
Indigenous breed 0.164* 0.094 -0.007 0.012
Agricultural information 0.045 0.079 -0.011 0.010
Access to vet services 0.047 0.098 0.024* 0.013
Age -0.005*** 0.002 0.001*** 0.000
Share sold to BMC -0.083 0.155 0.045** 0.020
Controlled breeding method -0.298* 0.178 0.039* 0.024
FMD region -0.019 0.072 -0.003 0.010
Non farm income -0.012 0.009 0.003* 0.002
Distance to market 0.043 0.033 -0.008* 0.005
Crop land size -0.101* 0.058 -0.007 0.005
Income X education -0.002* 0.001
Table2: Determinants 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 0.77 and TE is 0.45 .
- Controlled cattle breeding method, access to Vet services and market contract (BMC), off-farm income, herd size and farmers’ age all contribute positively to efficiency.
- On the contrary, 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, and/or encourage them to employ skilled farm managers.
- 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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Export of beef from Botswana (2000-2011)
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110
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