Presented by Sirak Bahta at the Tropentag 2014 Conference on Bridging the gap between increasing knowledge and decreasing resources, Prague, 17−19 September 2014

1. Technical efficiency in beef cattle production in
Botswana: a stochastic metafrontier approach
Sirak Bahta
Tropentag 2014: Bridging the gap between increasing knowledge
Sirak Bahta
and decreasing resources, Prague, 17−19 September 2014
International Livestock Research Institute (ILRI)

2. Outline
 Background
 Objective of the study
 Literature Review
 Data and Methodological Approach
 Results and discussion
 Conclusion and Policy Implications

3. 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

4. Cont. Background
(Cont.)
 Beef is dominant within the Botswana livestock
sector
3

5. Background
3,060
1,788
(Cont.)
2,247
3500
3000
2500
2000
1500
1000
500
0
'000
Commercial
Traditional
 Dualistic structure of production, with communal dominating
Cattle population
4

6. Background
(Cont.)
 Despite the numerical dominance , productivity is low esp. in
the communal/traditional sector
5
Sales
0.18
0.15
0.12
0.09
0.06
0.03
0
Home Slaughter
Deaths
GivenAway
Eradication
Losses
Commercial
Traditional

7. Background
 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
(Cont.)
6

8. Objective of the study
• 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
7

9. Literature review (cont..)
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)
8

10. 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 mixing
technological differences with technology-specific inefficiency
(Tsionas, 2002).
Various alternatives have been proposed to account for differences
in technology and production environment.
9

11. Literature review (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.
10

12. Literature review (Cont..)
Figure 1: Metafrontier illustration
Source: Adapted from Battese et al. (2004).
11

13. Data and Methodological Approach
Study Area
• Household data, collected by survey
• More than 600 observations (for this study classified by farm types)
12

14. Data and Methodological Approach
- Stochastic frontier analysis (Frontier 4.0)
- Linear Programing (SHAZAM)
- Bootstrapping to derive standard deviations of
metafrontiers (SHAZAM)
- Tobit (TE effects)- STATA
13

15. Results and discussion
Production function estimates
Table1: Production function estimates
Variable
Pooled 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
14

16. 39%
75% 76% 79% 77%
45%
49%
45%
80%
75%
70%
65%
60%
55%
50%
45%
40%
35%
30%
Cattle Farms Cattle & crop
farms
Cattle, Samll
Stcok & crop
farms
All farms
TE wrt
metafrontiier
Meta-technology
ratio
a
a
a
b c
b
Per cent
Results and discussion
Technology ratio and TE wrt to meta frontier
Table 1: Technical efficiency and meta-technology ratios
15

17. 70%
60%
50%
40%
30%
20%
10%
0%
all sample
catle
cattle-crop
cattle- crop-small stock
<0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1 1
Per cent
Results and discussion
TE wrt to meta frontier distribution
16

18. Results
Ddeterminants of technical efficiency
Table2: Determinants 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
17

19. Conclusion and policy implications
- 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.
18

20. Conclusion and policy implications
19
- 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).

21. Conclusion and policy implications
- 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.
20

22. better lives through livestock
ilri.org
The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is given to ILRI.

23. Export of beef from Botswana (2000-2011)
180000
160000
140000
120000
100000
80000
60000
40000
20000
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Quantity (tonnes)
Value (1000 $)