Improving Botswana Beef Farms' Efficiency
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Presented by Sirak Bahta (ILRI) at the Conference on Policies for Competitive Smallholder Livestock Production, Gaborone, Botswana, 4-6 March 2015
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Improving Botswana Beef Farms' Efficiency
- 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
- 3. 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 2
- 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
- 8. Literature review (Cont..) Source: Adapted from Battese et al. (2004). Figure 1: Metafrontier illustration 7
- 9. • Household data, collected by survey • More than 600 observations (for this study classified by farm types) Data and Methodological Approach Study Area 8
- 10. SFA Reject hypothesis Stop Accept hypothesis Linear programming/Shazam LR test TE effects/TobitTechnology Gaps Bootstraping/ Standard dev. Data and Methodological Approach
- 11. Results and discussion Production function estimates Variable Pooled 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 568 568 ϒ 0.99*** Log likelihood -518.63 456.66 Table1: Production function estimates 10
- 12. 35% 46% 57% 50% 46% 84% 81% 76% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Cattle farms Cattle and crop farms Mixed farms Total TE w.r.t. the meta-frontier Meta- technology ratio Percent Results and discussion Technology ratio and TE wrt to meta frontier Technical efficiency and meta-technology ratios 11
- 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. better lives through livestock ilri.org Ke a leboga!! Thank you !!
- 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
Notas do Editor
- 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