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Measurement of competitiveness in smallholder livestock systems and emerging policy advocacy: an application to Botswana Policies for Competetitive Smallholder.

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Presentation on theme: "Measurement of competitiveness in smallholder livestock systems and emerging policy advocacy: an application to Botswana Policies for Competetitive Smallholder."— Presentation transcript:

1 Measurement of competitiveness in smallholder livestock systems and emerging policy advocacy: an application to Botswana Policies for Competetitive Smallholder Livestock Production 4-6 March 2015 S. Bahta and P. Malope

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 OLSMLU Coefficientt-valuesCoefficientt-values Constant -34.87-26.31-38.12-32.49 Ln (Average Beef cattle price) 5.0128.23***5.5134.85*** Ln (Feed) -0.15-3.61***-0.13-3.11*** Ln (Veterinary) -0.12-2.97**-0.09-2.46** Ln (Labour.) 0.080.24**-0.79-1.93** Ln (fixed capital.) -0.02-0.640.020.53 Ln (Family labour Hours) -0.060.28*0.461.82* Ln (Crop land area) 0.552.95*0.281.70*

9 Results – Efficiency drivers Variables Coefficientt-values Constant -11.86-2.47** Age of household head -1.26-3.44*** Education of Household head 0.0430.14 Annual household non-farm income 0.262.64*** Distance market (commonly used) 0.562.42** Herd size 2.484.92*** Gender (% female farmers) -2.82-2.43*** Information access (Yes=1, No=0) 4.152.80*** 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.

12 THANK YOU


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