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 ProductionGaborone, Botswana, 4-6 March 2015

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

3,060

1,788

2,247

0

500

1000

1500

2000

2500

3000

3500

'00

0

Commercial

Traditional

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

0

0.03

0.06

0.09

0.12

0.15

0.18

Sales

Home Slaughter

Deaths

GivenAway

Losses

Eradication

Commercial

Traditional

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 $)

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

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Measuring efficiencyMeasuring 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-frontierEnables estimation of technology gaps for different groupsIt captures the highest output attainable, given input (x) and common technology.

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Literature review (Cont..)

Source: Adapted from Battese et al. (2004).

Figure 1: Metafrontier illustration

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

Data and Methodological ApproachStudy Area

8

SFA

Reject hypothesis

Stop

Accept hypothesis

Linear programming/Shazam

LR test

TE effects/TobitTechnology GapsBootstraping/ Standard dev.

Data and Methodological Approach

Results and discussionProduction 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

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35%

46%

57%

50%46%

84%81%

76%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Cattle farms Cattle andcrop farms

Mixed farms Total

TE w.r.t. themeta-frontier

Meta-technologyratio

Pe

r ce

nt

Results and discussionTechnology ratio and TE wrt to meta frontier

Technical efficiency and meta-technology ratios

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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

ResultsDeterminants of technical efficiency

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- 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

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Conclusion and policy implications

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- 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

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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 dataThe stochastic metafrontier estimation involves firstfitting individual stochastic frontiers for separate groupsand then optimising them jointly through an LP or QPapproach.- It captures the highest output attainable, given input (x)and common technology.

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Measuring efficiency

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

ResultsDdeterminants of technical efficiency

Table2: Determinants of technical efficiency

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