Technical efficiency in beef cattle production in Botswana: a stochastic metafrontier approach

Technical efficiency in beef cattle production in Botswana: a stochastic metafrontier approach

Sirak Bahta International Livestock Research Institute (ILRI)

Sirak Bahta

Tropentag 2014: Bridging the gap between increasing knowledge and decreasing resources, Prague, 17−19 September 2014

Outline

Background Objective of the study Literature Review Data and Methodological Approach Results and discussion Conclusion and Policy Implications

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

Cont.Background(Cont.)

Beef is dominant within the Botswana livestock sector

3

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 population4

Background(Cont.)

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

5

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 ))

Problems in production and marketing into export channels

• High transaction costs• Farmers’ preferences for keeping animals to an

advanced age• Lack of understanding of the various markets’ quality

requirements

Background(Cont.)

6

• To derive a statistical measure of Technical efficiency of 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 analyse the determinants of farmers’ TE• Come up with policy recommendations to

improve competitiveness of beef production

Objective of the study

7

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)

Literature review (cont..)

8

Literature review (cont..)

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 mixingtechnological differences with technology-specific inefficiency (Tsionas, 2002).

Various alternatives have been proposed to account for differences in technology and production environment.

9

MetafrontierThis 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.

Literature review (cont..)

10

Literature review (Cont..)

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

Figure 1: Metafrontier illustration

11

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

Data and Methodological ApproachStudy Area

12

- Stochastic frontier analysis (Frontier 4.0)

- Linear Programing (SHAZAM)

- Bootstrapping to derive standard deviations of

metafrontiers (SHAZAM)

- Tobit (TE effects)- STATA

Data and Methodological Approach

13

Results and discussionProduction function estimates

VariablePooled Stochastic frontier Metafrontier

Constant (β0 ) 7.04** 7.62***0.188 0.010

Feed Equivalents(β1 ) 0.22** 0.022***0.009 0.001

Veterinary costs(β2 ) 0.106*** 0.75***0.019 0.002

Divisia index (β3 ) 0.091*** 0.003***0.013 0.000

Labour (β4 ) 0.31** 0.008***0.015 0.001

Land(β5 ) 0.291*** 0.315***0.058 0.050

σ2 0.473***0.03

ϒ 0.987***Log likelihood -529.73 440.75

Table1: Production function estimates

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Cattle Farms Cattle & crop farms

Cattle, Samll Stcok & crop

farms

All farms30%

35%

40%

45%

50%

55%

60%

65%

70%

75%

80%

39%

45%

49%

45%

75% 76%79%

77%

TE wrt metafrontiier

Meta-tech-nology ratio

a

aa

cb

b

Per c

ent

Results and discussionTechnology ratio and TE wrt to meta frontier

Table 1: Technical efficiency and meta-technology ratios

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<0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1 10%

10%

20%

30%

40%

50%

60%

70% all sample

catle

cattle-crop

cattle- crop-small stock

Per c

ent

Results and discussionTE wrt to meta frontier distribution

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ResultsDdeterminants of technical efficiency

  SFA Tobit

 Variables Coefficient St Dev Coefficient St Dev

constant 3.71*** 0.250 0.446 0.030

Herd size -0.031*** 0.001 0.001*** 0.000

Indigenous breed 0.164* 0.094 -0.007 0.012

Agricultural information 0.045 0.079 -0.011 0.010

Access to vet services 0.047 0.098 0.024* 0.013

Age -0.005*** 0.002 0.001*** 0.000

Share sold to BMC -0.083 0.155 0.045** 0.020

Controlled breeding method -0.298* 0.178 0.039* 0.024

FMD region -0.019 0.072 -0.003 0.010

Non farm income -0.012 0.009 0.003* 0.002

Distance to market 0.043 0.033 -0.008* 0.005

Crop land size -0.101* 0.058 -0.007 0.005

Income X education -0.002* 0.001

Table2: Determinants 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 0.77 and TE is 0.45 .

- Controlled cattle breeding method, access to Vet services and market contract (BMC), off-farm income, herd size and farmers’ age all contribute positively to efficiency.

- On the contrary, 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, and/or encourage them to employ skilled farm managers.

- 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

20

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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Export of beef from Botswana (2000-2011)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110

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Value (1000 $)