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ANALYSIS OF PERFORMANCE AND
EFFICIENCY OF PEARL MILLET
(Pennisetum glaucum (L.) R .Br. ) MARKET
VALUE CHAIN: A CASE OF MBE...
OUTLINE
•Introduction
•Statement

of the problem

•Objectives
•Study

area
•Sample design and sampling procedure
•Methodol...
INTRODUCTION
Millets 4th important cereal in cultivation and production
in the tropics
•

•Coverage
•

15 and 12 m ha (Afr...
INTRODUCTION CONT’D
•Marketing

challenges;

•Poorly developed and fragmented markets with weak
value chains,
•High assemb...
INTRODUCTION CONT’D
•Trends

show reduction in acreage and
productivity/ha;
•Acreage

reduced; 115,302.6 ha (2007) to 100,...
INTRODUCTION CONT’D
•Promotional

efforts:
•Non-traditional crops project - acceptability and consumption (GoK)
•EPHTFC
•H...
STATEMENT OF THE PROBLEM
•Despite

the efforts, weak supply networks and
independent working relationship between actors a...
GENERAL OBJECTIVE
•To

improve the competitiveness and productivity of pearl
millet for the benefit of the farming communi...
SPECIFIC OBJECTIVES
•

To conduct value chain mapping of the pearl millet
marketing system connecting production areas of
...
STUDY AREA: MBEERE DISTRICT, KENYA
•ASAL

area

•Rainfall

–(640 - 1110 mm; <750 mm p.a)

•Temperature;
•Major

(20-300C ;...
SAMPLING AND SAMPLE SIZE
•255

market actors (120 Farmers; 25Traders; 2 Brokers; 8
Processors; 100 Customers) interviewed
...
DATA ANALYSIS
•Objective

1: Value chain mapping - descriptive statistics

•Objective

2: Market channel efficiency –marke...
RESULTS AND DISCUSSION: SOCIO-ECONOMIC XTS
Percentage Distribution

Variables

Farmers

Traders

Consumers

52.4 (15.94)

...
MEANS OF ACCESSING MARKET INFORMATION:
PRODUCERS
Percentage of respondents

Buyer
Daily prices

Market

information

days
...
MAPPING OF THE PEARL MILLET VALUE CHAIN
AND MARKETING CHANNELS
Producer

Rural agents
Traders

Small scale processors
Brok...
MARKETING EFFICIENCY OF DIFFERENT
PEARL MILLET MARKETING CHANNELS
Prices/Costs
Value added
Cost of marketing

Channel
Chan...
MARKET UPGRADING CONSTRAINTS:
TRADERS
Upgrading constraints
Most effective

Effective

Neither-nor

Specific constraints

...
ESTIMATES OF MEAN WTP MODEL
Variable

Coefficient estimate

Standard P- value
error

Constant (α)

9.235

1.662

0.000

Bi...
ESTIMATED LOGIT MODEL RESULT
WTP

Coefficient

Standard

Z

P>|z|

error

Effects

HHhead

-0.555

AgeofHH

0.080

0.029

...
CONCLUSION
•Channel

levels: three level (channel 1st, 2nd and 3rd); a
two level (4th channel); one level (5thchannel) and...
POLICY RECOMMENDATION
•Government

to enact policies -linking producers’
to processors for chain efficiency and profitabil...
ACKNOWLEDGEMENT
•Egerton
•AERC

University

(CMAAE program)

•ASARECA

pearl millet project (Field data collection)

•Supe...
PUBLICATIONS UNDER REVIEW
•Okech,

S.O, Ngigi, M. and Kimurto, P.K. (2013).
Value chain mapping on pearl millet in Kenya.
...
THANK YOU
END
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Analysis of Performance and Efficiency of Pearl Millet (Pennisetum glaucum (L.) R .Br. ) MARKET VALUE CHAIN: A CASE OF MBEERE DISTRICT, KENYA

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Pearl millet commercialization has the potential to support the food insecure rural households residing in marginal areas of Kenya. Despite the numerous program attempts in improving its productivity and market potential within Mbeere district, thousands are still food insecure. Poor coordination, overcrowding of actors activities and limited marketing opportunities has resulted in weak market value chains and underdeveloped output markets which have diluted commercialization initiatives. This study examined the performance and efficiency of pearl millet market value chain in order to improve its competitiveness and productivity for the benefit of the farming communities in ASALs of Kenya. Specifically, this study analyzed the existing pearl millet market value chain and its efficiency, identified traders key marketing constraints and determined consumers’ willingness to pay for value added pearl millet products. A multistage sampling technique was used to collect information from 255 market actors using a semi structured questionnaire and analysis done using descriptive statistics, marketing margin analysis and Contingent valuation methodology. Result showed that majority of the actors had a mean age between 42- 52 years. Most producers (62.5%) were males while females (100%) concentrated in pearl millet marketing activities. Empirical findings showed that, producers share of the final consumer price was 23.3% with processors having a higher margin compared to traders and producers despite their limited functions. Transport cost, police bribes, border taxes, rent and commission charges formed major components of marketing costs. Most consumers (60%) were willing to pay a premium price of 42% above the normal market price of Kshs 100 for value added pearl millet products. Age, number of children below 12 years in a household, gender of household head, income and awareness were important factors that positively influenced consumers WTP premium prices.

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  • Eastern Province Horticulture and Traditional Food crops (EPHTFC)
  • Past studies; Obare et al. 2006; IFPRI 2010; Gudmundssonet al., 2006
  • Channel 1: Producers-Rural agent-Traders-Small processors-Consumers  2: Producers-Traders-Brokers-Large processors-Consumers  3: Producers-Rural agents-Traders-Large processors-Consumers  4: Producers-Rural agent-Traders-Consumers
  • Statistics: -Number of observations = 100; Prob.&gt; chi square = 0.0000; Log likelihood = -33.660; Pseudo R-squared = 0.4014; Likelihood ratio test of zero slope coefficients= 45.15; *:
  • Transcript of "Analysis of Performance and Efficiency of Pearl Millet (Pennisetum glaucum (L.) R .Br. ) MARKET VALUE CHAIN: A CASE OF MBEERE DISTRICT, KENYA"

    1. 1. ANALYSIS OF PERFORMANCE AND EFFICIENCY OF PEARL MILLET (Pennisetum glaucum (L.) R .Br. ) MARKET VALUE CHAIN: A CASE OF MBEERE DISTRICT, KENYA SILAS OKECH ONGUDI SUPERVISORS Dr. Ngigi, M. Dr. Kimurto, P.
    2. 2. OUTLINE •Introduction •Statement of the problem •Objectives •Study area •Sample design and sampling procedure •Methodology •Results and discussion •Conclusion and policy recommendation
    3. 3. INTRODUCTION Millets 4th important cereal in cultivation and production in the tropics • •Coverage • 15 and 12 m ha (Africa and Asia) Pearl Millet (Major): Merits; •Hot and dry conditions (200-600 mm p.a); •Requires 25% less rainfall •Diet >500 m households; •Feed source •Fuel and ethanol production
    4. 4. INTRODUCTION CONT’D •Marketing challenges; •Poorly developed and fragmented markets with weak value chains, •High assembly and processing costs, •Uncompetitive grain prices, •Lack of market information • Limited processing facilities, •Lags in legal and policy framework
    5. 5. INTRODUCTION CONT’D •Trends show reduction in acreage and productivity/ha; •Acreage reduced; 115,302.6 ha (2007) to 100,143.9 ha (2011) •Yield/Ha decline1,610 kg in (1980) to 200-800 kg (2008) - potential of 1,500-3,000 kg ha-1
    6. 6. INTRODUCTION CONT’D •Promotional efforts: •Non-traditional crops project - acceptability and consumption (GoK) •EPHTFC •HOPE project-income and food security (IFAD & GoK) project- productivity and marketing challenges (ICRISAT) •INTSORMIL/B&M marketing ESA •ASARECA pearl Gates - millets and sorghum production and millet productivity project •However, •<3% pearl millet enters formal production channels •2 m tons pearl millet is fed to animals compared to 30 m tons of sorghum
    7. 7. STATEMENT OF THE PROBLEM •Despite the efforts, weak supply networks and independent working relationship between actors are major concern. •Yet, improved market value chain, efficient collaboration, networking and coordination are important. •Past studies -efficient coordination has the potential of improving market demand, producers’ output value, stimulate adoption and production •Nevertheless, pearl millet marketing and value chain potential, coordination and collaboration is limited or non existence at all.
    8. 8. GENERAL OBJECTIVE •To improve the competitiveness and productivity of pearl millet for the benefit of the farming communities in Arid and Semi-Arid Lands of Kenya
    9. 9. SPECIFIC OBJECTIVES • To conduct value chain mapping of the pearl millet marketing system connecting production areas of Mbeere district and the final markets of Kenya • To evaluate the marketing channel efficiency of pearl millet and the benefits accruing from farm gate to final consumers • To identify major marketing constraints affecting pearl millet traders in Mbeere district of Kenya • To determine consumers’ willingness to pay for value added pearl millet products within markets of Kenya
    10. 10. STUDY AREA: MBEERE DISTRICT, KENYA •ASAL area •Rainfall –(640 - 1110 mm; <750 mm p.a) •Temperature; •Major (20-300C ; >300C (March)) crop failures (maize).
    11. 11. SAMPLING AND SAMPLE SIZE •255 market actors (120 Farmers; 25Traders; 2 Brokers; 8 Processors; 100 Customers) interviewed •Purposive sampling technique of Siakago and Evurore Q-administration •Farmers: simple random sampling •Intermediaries(B/A) ; snowball sampling •Traders: simple random sampling •Processors: MoA records •Consumers: simple random sampling
    12. 12. DATA ANALYSIS •Objective 1: Value chain mapping - descriptive statistics •Objective 2: Market channel efficiency –marketing margin and efficiency calculation •Objective •Objective 3: Marketing constraints – descriptive statistics 4: Consumers WTP- Semi double bound contingent valuation
    13. 13. RESULTS AND DISCUSSION: SOCIO-ECONOMIC XTS Percentage Distribution Variables Farmers Traders Consumers 52.4 (15.94) 41.56 (11.26) 45.42 (11.74) Illiterate 12.5 - 5 Primary 58.9 44 52 - 56 31 Tertiary 26.6 - 9 University 1.67 - 3 Male 62.5 - 61 Female 37.5 100 39 Full time - 72 18 Part time - 24 21 Unemployed - - 55 Housekeeper - - 4 Retired - - 2 Mean age in years Educational level Secondary Gender Respondents Employment status
    14. 14. MEANS OF ACCESSING MARKET INFORMATION: PRODUCERS Percentage of respondents Buyer Daily prices Market information days Mobile phones 39.17 5.83 32.5 Internet 0.83 2.5 0 Magazine 1.67 6.83 0 Radio 16.67 10.83 17.5 Via fellow farmers 1.67 5.83 9.17 Television 39.17 0 0 Limited access 1.83 74.17 40 Total 100 100 100
    15. 15. MAPPING OF THE PEARL MILLET VALUE CHAIN AND MARKETING CHANNELS Producer Rural agents Traders Small scale processors Brokers Large processors Supermarket Final consumer Channel 1:- Producer- Rural agents- Traders- small processors- Consumers; Channel 2:Producers-Traders- Brokers- Large processors- Final consumers; Channel 3:- Producers- Rural agents- Traders- Brokers- Large processors- Final consumers; Channel 4: producers- rural agent- traders- final consumers; Channel 5:- Producers- Final consumers
    16. 16. MARKETING EFFICIENCY OF DIFFERENT PEARL MILLET MARKETING CHANNELS Prices/Costs Value added Cost of marketing Channel Channel I Channel II Channel III IV 6,080 11,600 15,200 4,060 Traders 1,392.60 1,392.60 1,392.60 1,392.60 Brokers - 1,914 1,914 - Large processors - 940 940 - Small processors 415 - - - 1,807.60 4,246.60 4,246.60 1,392.60 3.36 2.70 3.54 2.90 Total cost of marketing Marketing Efficiency index Channel 1:- Producer- Rural agents- Traders- small processors- Consumers; Channel 2:- Producers-Traders- Brokers- Large processors- Final consumers; Channel 3:- Producers- Rural agents- Traders- Brokers- Large processors- Final consumers; Channel 4: producers- rural agent- traders- final consumers; Channel 5:- Producers- Final consumers
    17. 17. MARKET UPGRADING CONSTRAINTS: TRADERS Upgrading constraints Most effective Effective Neither-nor Specific constraints Precautionary savings Moderately effective Ineffective 44 20 24 12 32 Warehouse receipts 28 20 20 Forward contracts 32 24 44 12 4 Insurance 8 56 20 0 10 20 30 40 Percentage respondents 50 60
    18. 18. ESTIMATES OF MEAN WTP MODEL Variable Coefficient estimate Standard P- value error Constant (α) 9.235 1.662 0.000 Bid (ρ) 0.065 0.013 0.000 Mean WTP (α/ρ) 142.077 Number of observations = 100; Log likelihood = -63.862 • On average wtp Kshs.142 represented a premium price of 42% over the base price of Kshs 100 of finger millet product.
    19. 19. ESTIMATED LOGIT MODEL RESULT WTP Coefficient Standard Z P>|z| error Effects HHhead -0.555 AgeofHH 0.080 0.029 2.76 0.006* 0.008 Gender 1.252 0.728 1.72 0.086*** 0.127 EducLevel -0.102 0.348 -0.29 0.769 -0.010 NoChildren 0.558 2.29 0.022** 0.057 Employmentstatus 0.238 0.277 0.86 0.390 0.024 Income 1.029 0.388 2.65 0.008* 0.105 Awareness 1.351 0.001* 0.138 HeardProduct 0.229 0.455 0.50 0.615 0.023 -10.540 3.136 -3.36 0.001 - Constant 0.716 Marginal -0.78 0.244 0.420 3.22 0.438 -0.056
    20. 20. CONCLUSION •Channel levels: three level (channel 1st, 2nd and 3rd); a two level (4th channel); one level (5thchannel) and a zero level (6th channel) •Most efficient channel: channel III (3.54) while the least channel II (2.70) •Higher transport costs (brokers and traders)- (police bribes, municipal cess and import taxes) •Major procurement constraints: lack of targeted insurance products (56%) and limited use of contract (44%) •Consumer WTP >42% price premium (income and HH composition and prior knowledge)
    21. 21. POLICY RECOMMENDATION •Government to enact policies -linking producers’ to processors for chain efficiency and profitability •Municipal removed cess, border taxes and police bribes be •Targeted insurance products (crop insurance) be introduced •Fast food marketers should be familiar with price premium and adjust their marketing strategies (consumer classes)
    22. 22. ACKNOWLEDGEMENT •Egerton •AERC University (CMAAE program) •ASARECA pearl millet project (Field data collection) •Supervisors (Dr. Ngigi and Dr. Kimurto) •Lecturers •Classmates and friends
    23. 23. PUBLICATIONS UNDER REVIEW •Okech, S.O, Ngigi, M. and Kimurto, P.K. (2013). Value chain mapping on pearl millet in Kenya. ASARECA regional conference (Nakuru, February 2013) •Okech, S.O, Ngigi, M. and Kimurto, P.K. (2013). Consumers’ Willingness to Pay for Value Added Pearl Millet Products within the Markets of Kenya: A One and One Half Bound Dichotomous Choice Contingent Valuation- (Asian Journal of Agricultural Sciences)
    24. 24. THANK YOU END
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