Technical efficiency and technological gaps among smallholder beef farms in Botswana: a stochastic meta-frontier approach POLICIES FOR COMPETETIVE SMALLHOLDER LIVESTOCK PRODUCTION 4-6 MARCH 2015, GABORONE, BOTSWANA Sirak Bahta International Livestock Research Institute (ILRI)

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

 Dualistic structure of production, with communal dominating Background (Cont.) Cattle population 2

Background (Cont.)  Despite the numerical dominance, productivity is low esp. in the communal/traditional sector 3

 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

 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

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

Literature review (Cont..) Source: Adapted from Battese et al. (2004). Figure 1: Metafrontier illustration 7

Household data, collected by survey More than 600 observations (for this study classified by farm types) Data and Methodological Approach Study Area 8

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

Results and discussion Production function estimates Variable Pooled Stochastic frontierMetafrontier Constant (β0 )10.6**7.46*** Feed Equivalents(β1 )0.10**0.20*** Veterinary costs(β2 )0.40***0.21*** Divisia index (β3 )0.30**0.50*** Labour (β4 ) *** σ20.45*** 0.03 N568 ϒ 0.99*** Log likelihood Table1: Production function estimates 10

Results and discussion Technology ratio and TE wrt to meta frontier Technical efficiency and meta-technology ratios 11

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

-The majority of farmers use available technology sub-optimally and produce far less than the potential output; average MTR is and TE is 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

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).

-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

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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

SFATobit VariablesCoefficientSt DevCoefficientSt Dev Constant (β0) 3.71*** ***0.030 Beef herd size (δ1) *** ***0.000 Indigenous breed (δ2) 0.21*** ***0.012 Non-farm income (δ3) 0.01*** *** Age of farmer (δ4) -0.01** ** Gender (% female farmers)(δ5) Sales to BMC (δ6) *** Controlled breeding method (δ7) -0.35** *** Distance to commonly used market (Kms)(δ8) *** Other agricultural income (% of farmers)(δ9) *** Income-education (δ10) * Results Ddeterminants of technical efficiency Table2: Determinants of technical efficiency 15