A metafrontier analysis of determinants technical efficiency in beef farm types: An application to

Botswana

Sirak BahtaInternational 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 productionCattle production is the only source of

agricultural exports

1

Cont.Background(Cont…)

Beef is dominant within the Botswana livestock sector

2

0

500

1000

1500

2000

2500

3000

35003,060

1,788

2,247

'000

Commercial

Traditional

Dualistic structure of production, with communal dominating

Background(Cont…)

Cattle population3

Background(Cont…)

Despite the numerical dominance , productivity is low esp. in the communal/traditional sector

4

Sales

Home Slaughter

Deaths

GivenAway

Losses

Eradication

0

0.03

0.06

0.09

0.12

0.15

0.18

CommercialTraditional

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…)

5200020012002200320042005200620072008200920102011

0

30000

60000

90000

120000

150000

180000Quantity (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

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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

dataThe 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

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Theoretical framework(cont…)

• Household data, collected by survey• More than 600 observations (for this study classified by farm types)

Data and Methodological ApproachStudy Area

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SFA

Accept hypothesis Reject hypothesis

Linear programming/Shazam

LR test

TE effects/TobitBootstraping/ Standard dev.

Data and Methodological Approach

12

Stop

Technology Gaps

Results and discussion cont…

A

Technical efficiency and meta-technology ratios

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Cattle farms

Cattle and crop

farms

Mixed farms*

All Farms0%

20%

40%

60%

80%

100%

35%46%

57%50%46%

84% 81%76%

TE w.r.t the meta-frontier

Meta-tech-nology ratio

C B ABA A

Results and discussionProduction function estimates

Variable MetafrontierConstant (β0 ) 7.46***

0.0001Feed Equivalents(β1 ) 0.20***

0.0001Veterinary costs(β2 ) 0.21***

0.0001Divisia index (β3 ) 0.50***

0.00029Labour (β4 ) 0.10***

0.0001Log likelihood 456.66

Table1: Production function estimates

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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/Education

- Ve

+ Ve

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• 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

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• 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…

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Conclusion and policy implications cont…

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- 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…

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Next Steps

• Latent class stochastic frontiers• System Dynamics