A metafrontier analysis of determinants technical
efficiency in beef farm types: An application to
Botswana
Sirak Bahta
International Conference of Agricultural Economist (ICAE)
Milan, Italy, 9-14 August 2015
•

Background
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
1

Cont.
Background
(Cont…)
 Beef is dominant within the Botswana
livestock sector
2

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
3

Background
(Cont…)
 Despite the numerical dominance , productivity is low
esp. in the communal/traditional sector
4
0
0.03
0.06
0.09
0.12
0.15
0.18
Sales
Home
Slaughter
Deaths
GivenAway
Losses
Eradication
Commercial
Traditional

 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…)
5
0
30000
60000
90000
120000
150000
180000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Quantity (tonnes) Value (1000 $)
US$

 To derive a statistical measure of Technical
efficiency and meta technology gap ratio
(MTR) for 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 analyze the determinants of farmers’ TE
• Come up with policy recommendations to
improve competitiveness of beef production
Objective of the study
6

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)
Theoretical framework
7

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 mixing technological
differences with technology-specific inefficiency (Tsionas,
2002).
Various alternatives have been proposed to account for
differences in technology and production environment.
8
Theoretical framework
(cont…)

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.
9
Theoretical framework
(cont…)

Source: Adapted from Battese et al. (2004).
Figure 1: Metafrontier illustration
10
Theoretical framework
(cont…)

• Household data, collected by survey
• More than 600 observations (for this study classified by farm types)
Data and Methodological Approach
Study Area
11

SFA
Accept
hypothesis Reject hypothesis
Linear
programming/Shazam
LR test
TE effects/Tobit
Bootstraping/
Standard dev.
Data and Methodological Approach
12
Stop
Technology Gaps

Results and discussion cont…
A
Technical efficiency and meta-technology ratios
13
35%
46%
57%
50%46%
84% 81%
76%
0%
20%
40%
60%
80%
100%
Cattle
farms
Cattle and
crop farms
Mixed
farms*
All Farms
TE w.r.t the
meta-frontier
Meta-technology
ratio
C B AB
A A

Results and discussion
Production function estimates
Variable Metafrontier
Constant (β0 ) 7.46***
0.0001
Feed Equivalents(β1 ) 0.20***
0.0001
Veterinary costs(β2 ) 0.21***
0.0001
Divisia index (β3 ) 0.50***
0.00029
Labour (β4 ) 0.10***
0.0001
Log likelihood 456.66
Table1: Production function estimates
14

Results and discussion cont…
Technical
efficiency
 Beef herd size
 Sales to BMC
 Controlled
breeding
method
 Other agric-
income
 Farmer age
 Distance to
commonly Used
market
 Indigenous
breed
 Income/Ed
ucation
- Ve
+ Ve
15

• The majority of farmers use available
technology sub-optimally and produce far less
than the potential output; average MTR is
76% and TE is 50%.
• Herd Size, Controlled cattle breeding
method, market contract (BMC), other
agricultural income and Farmer age and
distance to market, all contribute positively
to efficiency.
Conclusion and policy implications
16

• On the contrary, proportion of indigenous
cattle and interaction of income and formal
education did not have a favorable influence
on efficiency.
• 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.
Conclusion and policy implications
cont…
17

Conclusion and policy implications
cont…
18
- 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 programs).
- 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, would also contribute to improving
efficiency among Botswana beef farmers.
Conclusion and policy implications
cont…
19

Next Steps
• Latent class stochastic frontiers
• System Dynamics