Technical efficiency and technological gaps among smallholder beef farms in Botswana: A stochastic meta-frontier approach
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. 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
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. 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. 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. 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
9. • Household data, collected by survey
• More than 600 observations (for this study classified by farm types)
Data and Methodological Approach
Study Area
8
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. - 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. 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. - 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. The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is given to ILRI.
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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. 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
Notes de l'éditeur
Agricultural contribution to GDP is about 2-3%.
Not clear as to whether beef production is competitive
Studies have relied on household budget analysis and limited household data
Others have concentrated on productivity of agriculture
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)
The metafrontier function captures the highest possible output level (y) attainable, given the input (x) and common technology in the industry (Figure 1).
Output levels for producers who are efficient both in respective group frontiers (e.g., frontier 1) and in the entire industry lie on the metafrontier. Frontiers 2 and 3 fall below the metafrontier; this implies that they represent efficient production in the groups/production systems, but not so for the industry.
Such analysis at the level of beef farm type is proposed as desirable because it is likely that these farms are operating with different technologies. It is also expected that differences in technology and organization, as well as asset ownership and human capital both within and between these beef farm types could cause or underlie significant differences in the technologies used by the farms.
From the policy point of view, it is of interest for the study to distinguish the beef farm type differences in their mean efficiency levels, technology gaps and identify common determinants of technical efficiency. These assertions require statistical testing, as there would be no good reason for estimating the efficiency levels of beef farm types relative to a meta-frontier production function if these farmers are found out to operate under the same technology (Battese et al, 2004). A likelihood-ratio (LR)7 test of the null hypothesis, that the beef farm type stochastic frontier models are the same for all farms in Botswana, was calculated after estimating the stochastic frontier by pooling the data from all beef farm types. The value of the LR statistic was 76.2 which is highly significant (Kodde and Palm, 1986).
Consistent with assumed producer rationality, the estimated input parameters are all positive and the elasticities fulfil the regularity condition of monotonicity which implies the production frontiers are non-decreasing in inputs. That is an increase in the application of any of the inputs would significantly increase output.
Table further shows that, the value of is significant, which implies that the frontier model is stochastic (rather than deterministic). Moreover the estimated value of γ is significantly different from zero, implying that 99 per cent of the discrepancies between the observed value of beef output and the frontier output can be attributed to failures within the farmers’ control.
The mean meta-technology ratio (MTR) in the whole sample is 0.76; with about 96 per cent of farmers across the three beef production systems having MTR estimates below 1. This implies that, on average, beef farmers in Botswana produce 76 per cent of the maximum potential output achievable from the available technology. Moreover most of farmers, about 96 per cent, have MTR estimates below 1, which indicate that they use the available technology, such as use of cross breeds, sub-optimally. This could be due to low rates of adoption or poor utilization of adopted technologies influenced by, as described above, the quality of extension services they receive.
TABLE 3
The average MTR is high in beef farmers who are also engaged in other agricultural activities (crop and small stock farming). This is somehow consistent with the differences in relative levels of investments in the cattle enterprise by farmers in the three production systems (indicated in Table 2). It is interesting to note that in all but cattle only farms, the value of the maximum meta-technology gap ratio obtained is 1 (Max MTR=1) which indicates that their group frontiers are tangent to the meta-frontier (Battese et al., 2004). Therefore, more access to better technology (e.g. cattle breeds or feed planning techniques) is necessary in order those farmers who use technology sub-optimally achieve further productivity gains. The study showed that 96 per cent of farmers across the three production systems have MTR estimates below 1, indicating that they use the available technology (e.g., crossbreed cattle) sub-optimally.
Perhaps this could be due to,
as noted by Diagne (2010), lack of awareness of the technologies and/or how to use them that lead to low rates of adoption or poor use of agricultural technologies in sub-Saharan Africa. Consistent with relative levels of investments in the three beef production systems (Table 2), the average MTR is higher in beef farms who additionally engage in either crop (0.84) or both crop and small stock production (0.81).
suggesting that there is considerable scope to improve beef production
It should be noted that in stochastic frontier estimation, the parameter for inefficiency level usually enters the model as the dependent variable in the inefficiency effects component of the model. This, therefore, means that a positive sign of the coefficient of efficiency driver variable (in the M-vector) implies inefficiency. On the contrary, a negative sign of the coefficient is interpreted as potentially having a positive influence on efficiency (Brummer and Loy, 2000; Coelli et al., 2005).
Various alternatives have been proposed to account for differences in technology and production environment.
Methods to address technology differences in efficiency estimation
Continuous parameters method
Bayesian stochastic frontiers that - assess the influence of exogenous factors on either the production function or inefficiency component (Van den Broeck et al. ,1994; and Koop et al. ,1997)
Nonparametric stochastic frontier
Nonparametric stochastic frontier based on local maximum likelihood approach (Kumbhakar et al. , 2007) ).
Predetermined sample classification
Classifying the data into various groups based on a priori information, and then separate frontiers are estimated for each group.
Latent class stochastic frontier
Uses of latent variable theory to classify the data into segments or groups, and then estimate a frontier for each group in one stage.
Metafrontier
Proposed by Battese et al. (2004) and estimated by specifying a single data generating process, which explains deviations between observed outputs and the maximum possible explained output levels in the group frontiers