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Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
Determinants of profit efficiency among smallholder beef producers in Botswana
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Determinants of profit efficiency among smallholder beef producers in Botswana

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Presentation by Sirak Bahta and Derek Baker at the International Food and Agribusiness Management Association (IFAMA) annual meeting, Cape Town, South Africa, 16-17 June 2014.

Presentation by Sirak Bahta and Derek Baker at the International Food and Agribusiness Management Association (IFAMA) annual meeting, Cape Town, South Africa, 16-17 June 2014.

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  • 1. Determinants of profit efficiency among smallholder beef producers in Botswana Sirak Bahta, International Livestock Research Institute (ILRI) Derek Baker, University of New England ARMIDALE , Australia International Food and Agribusiness Management Association (IFAMA) annual meeting Cape Town, South Africa 16-17 June 2014
  • 2. Outline  Background  Objective of the study  Literature review  Methodological approach  Results and discussion  Conclusion and policy implications 1
  • 3. 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 2
  • 4. Cont. Background (Cont.)  Beef is dominant within the Botswana livestock sector 3
  • 5. 2,247 0 500 1000 1500 2000 2500 3000 3500 '000 Commercial Traditional  Dualistic structure of production, with communal dominating Background (Cont.) Cattle population 4
  • 6. 0 0.05 0.1 0.15 0.2 0.25 Sales Home Slaughter Purchases Offtake BirthsDeaths GivenAway Losses Eradication Commercial Traditional Background (Cont.)  Despite the numerical dominance , productivity is low esp. in the communal/traditional sector 5
  • 7.  Growing domestic beef demand and on-going shortage of beef for export:  Beef export quota has been declining sharply (e.g. from 86 percent 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  Not clear as to whether beef production is competitive  Studies have relied on household budget analysis and limited household data  Others have concentrated on productivity of agriculture Background (Cont.) 6
  • 8. Objective of the study To derive a statistical measure of profit efficiency, thus competitiveness, of different herd size smallholder farmers  using a stochastic profit frontier approach Specifically the study seeks to: • Identify the determinants of profitability and efficiency drivers • Measure overall efficiency score of beef producers • Testing for the effects of farm size on efficiency both within and between size classes • Come up with policy recommendations to improve competitiveness of beef production 7
  • 9.  Competitiveness relate in one way or another to profitability  Sustained ability to profitably gain and maintain market share (Agriculture Canada, 1991) • Competitiveness can be measured at three levels, macro; meso and micro-levels • “the ability to sell products that meet demand requirements in terms of price, quality & quantity and at the same time ensure profits” Literature review 8
  • 10. • Productivity and efficiency : often cited as indicators or measures of competitiveness or profitability. • 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) Methodological Approach 9
  • 11. • Household data, collected by survey • More than 600 observations Methodological Approach Study Area 10
  • 12. Methodological Approach  Cobb-Douglas Profit frontier function 𝜋𝑖 = 𝑓(𝑝𝑖, 𝑍) 𝑒𝑥𝑝(𝑉𝑖 − 𝑈𝑖 )𝑖 = 1, 2, … . . 𝑛  Dependent variable = profit normalized by output price  Independent variable = Input prices normalized by output price(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) 11
  • 13. Results and discussion Descriptive statistics Variables HZ1 (N=238) HZ2 (N=140) HZ3 (N=178) Pooled (N=556) F- statistics Value of beef Cattle output (Pula per year) 2,076.4 58,60.1 11,215.8 5,955.0 0.000 Average Beef cattle price (Pula) 1,772.3 2,081.4 2,218.7 1,993.0 0.000 Feed cost (Pula per year) 264.6 782.71 922.18 605.6 0.002 Veterinary cost (Pula per year) 296.0 565.92 1,192.2 650.9 0.000 Paid labour cost (Pula per year) 1,450.5 2,765.83 4,797.8 2,853.3 0.000 Cost of other inputs (Pula per year) 3,53.9 725.36 1158.0 704.9 Value of fixed capital (Pula) 22,170.9 48,869.9 343,544.6 131,779.5 0.006 Total crop land area (Hectares) 4.9 6.88 7.30 6.19 0.198 Family labour (hours per month) 201.5 226.85 209.17 210.3 0.585 12
  • 14. Variables HZ1 (N=238) HZ2 (N=140) HZ3 (N=178) Pooled (N=556) F- statistics Age of household head (Years) 58.9 59.9 60.9 59.8 0.440 Gender (% female farmers) 31.1% 22.1% 10.1% 22.1% 0.000 Education of Household head (years) 4.1 4.24 6.62 4.95 0.000 Household Off farm income (Pula per year) 41,372.6 48,535.9 77,728.8 54,815.6 0.000 Distance to market(Km) 28.9 29.58 61.83 39.7 0.000 Herd size (Beef cattle equivalent) 5.3 14.45 55.99 23.9 0.000 Information access (Yes=1, No=2) 73.5% 79.3% 79.2% 76.8% 0.288 FMD disease zone (Yes=1, No=2) 36.6% 39.3% 53.9% 42.8% 0.001 Crop income (Yes=1, No=2) 43.3% 52.9% 51.7% 48.4% 0.112 Credit access 1.3% 2.9% 3.4% 2.3% 0.333 Note: 1 Pula = 0.1159 USD (FNB, 2013) Results and discussion Descriptive statistics(Cont.) 13
  • 15. Variables HS1 HS2 HS3 Pooled Constant -0.08 1.11 2.01 2.61 Ln (Feed) -0.24* -0.30** -0.23 -0.26*** Ln (Veterinary) -0.23 -0.43** -0.43** -0.44*** Ln (Labour.) -0.15*** -0.11 -0.18** -0.004 Ln (fixed capital.) 0.03** 0.02 0.02 0.01 Ln (Family labour Hours) 0.06* -0.07 -0.16* 0.003 Ln (Crop land area) 0.05 0.05** 0.08* 0.05***  0.21*** 0.44*** 0.69*** 0.44*** gamma 0.72*** 0.65* 0.68* 0.74*** log likelihood function -147.6*** -138.59*** -219.35*** -561.98*** LR test of the one-sided error -24.6 17.12186 21.20 70.25 Results:Stochastic profit frontier estimates Notes: statistical significance levels: ***1%; **5%; *10%.
  • 16. Results: Efficiency drivers Variables HZ1 HZ2 HZ3 Pooled Constant -0.10 0.30 2.09 2.80 Age of household head 0.15*** 0.89** 0.07 0.02 Education of Household head -0.01 0.02 -0.04** -0.04* Annual household non-farm income -0.02** 0.01 0.04 0.004 Distance market (commonly used) 0.02 -0.14* -0.12** -0.03** Herd size -0.09 -1.27** -0.53*** -0.23*** Gender (% female farmers) -0.13 -0.06 0.07 0.10 Information access (Yes=1, No=0) 0.10* 0.08 0.33 0.11* FMD disease zone (Yes=1, No=0) -0.35*** -0.16 0.09 0.04 Crop income (Yes=1, No=0) -0.03 -0.23 -0.31** -0.17*** Credit access (Yes=1, No=0) -0.14 0.26 -0.08 -0.17 Notes: statistical significance levels: ***1%; **5%; *10%. 15
  • 17. Results: efficiency scores 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 HZ1 (Mean=0.56) HZ2 (Mean=0.62) HZ3 (Mean=0.68) Pooled (Mean=0.58) Numberoffarms Efficiency score 16
  • 18. • In general the mean efficiency of 0.58 (for the pooled sample) implies that there is a substantial loss of profit due to inefficiency. • Profits could be increased through reduction in inputs costs and improved access to crop land • Examination of the influence of scale on profitability yielded mixed results : details of how boundaries are drawn between size classes are important and should be examined in further research • Presence of inefficiency in the study reminds that production models that assume absolute efficiency could lead to misleading conclusions. Conclusion and policy implications 17
  • 19. • Policies to improve farm profit should be directed at  Reducing input prices (encouraging farmers to engage in feed production and extend LACs services to large farmers with efficient control mechanism)  Improving access to crop land  Improving infrastructure such as roads (Currently only 45 percent of farmers have access to roads (IFPRI 2013) and collection points of livestock Encouraging farmers to engage in crop farming, particularly in feed production Training of farmers especially in marketing Encouraging young farmers in beef production Conclusion and policy implications 18
  • 20. Thank you!

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