Presented by S. Bahta (ILRI) and P. Malope (BIDPA) at the Conference on Policies for Competitive Smallholder Livestock Production, Gaborone, Botswana, 4-6 March 2015

1. Measurement of competitiveness in smallholder
livestock systems and emerging policy advocacy:
An application to Botswana
S. Bahta (ILRI) and P. Malope (BIDPA)
Conference on Policies for Competitive Smallholder Livestock Production
Gaborone, Botswana, 4-6 March 2015

2. Structure of Presentation
Introduction and objectives
Methodological approach
Results and discussion
Conclusion and policy implications

3. Introduction and background
• Agric. Dominated by livestock production, esp.
beef.
• Dual production systems, with communal
dominating
• Productivity low esp. in the communal sector
• Not clear as to whether beef production is
competitive
• Studies have relied on budgetary analysis and
limited household data
• Others have calculated comp. at macro level

4. Objectives
• Measure competitiveness of beef production using
household data
• Specifically the study seeks to:
• Identify the determinants of profitability
• Identify efficiency drivers
• Measure overall profit efficiency of beef production
• Come up with policy recommendations to improve
profitability of beef production

5. Competetiviness defined
• Competitiveness has many definitions
• Competitiveness can be measured at three levels,
macro; meso and micro-levels
• Study measure competitiveness at micro level
• Definition at micro level relate to profitability
• “the ability to sell products that meet demand
requirements in terms of price, quality & quantity and at
the same time ensure profits”

6. Methodology - Study area

7. Approach
• Translog profit frontier function
• Dependent variable = profit per beef equivalent
• Independent variable = weighted output price, Input
prices per beef equivalent (feed, veterinary and Labor),
Fixed costs per beef equivalent (Fixed capital, family
labor and Land)
• Efficiency drivers: household characteristics (Age,
Education, Gender, non-farm income, access to crop
farm income) and transaction cost variables (distance to
markets, access to agriculture/market information) and
location variable (FMD zone)

8. Results – Stochastic frontier estimates
Variables
OLS MLU
Coefficient t-values Coefficient t-values
Constant -34.87 -26.31 -38.12 -32.49
Ln (Average Beef
cattle
price) 5.01 28.23*** 5.51 34.85***
Ln (Feed) -0.15 -3.61*** -0.13 -3.11***
Ln (Veterinary) -0.12 -2.97** -0.09 -2.46**
Ln (Labour.) 0.08 0.24** -0.79 -1.93**
Ln (fixed capital.) -0.02 -0.64 0.02 0.53
Ln (Family labour
Hours) -0.06 0.28* 0.46 1.82*
Ln (Crop land
area) 0.55 2.95* 0.28 1.70*

9. Results – Efficiency drivers
Variables Coefficient t-values
Constant -11.86 -2.47**
Age of household head -1.26 -3.44***
Education of Household head 0.043 0.14
Annual household non-farm income 0.26 2.64***
Distance market (commonly used) 0.56 2.42**
Herd size 2.48 4.92***
Gender (% female farmers) -2.82 -2.43***
Information access (Yes=1, No=0) 4.15 2.80***
FMD disease zone (Yes=1, No=0) -4.56 -3.84***
Crop income (Yes=1, No=0) -2.31 -2.94***

10. Conclusions and policy
implications
• Profits could be increased through reduction in inputs
prices, increase in output price and access to crop
land.
• Presence of inefficiency in the study reminds that
production models that assume absolute efficiency
could lead to misleading conclusions.
• The mean efficiency of 0.56 implies that there is a
substantial loss of profit due to inefficiency.

11. Conclusions and policy
implications
Policies to improve farm profit should be directed at
• Enhancing producer prices as well as ways to reduce
input prices
• Improving infrastructure such as roads and collection
points of livestock
• Improving access to crop land and
• Encouraging farmers to engage in crop farming,
particularly in feed production.