FARMERS’ WILLINGNESS TO PAY FOR IRRIGATION WATER SYSTEM AS A MECHANISM FOR SUSTAINABLE WATERSHED MANAGEMENT AND ENHANCED FOOD PRODUCTION IN KERIO VALLEY BASIN KENYA

1. FARMERS’WILLINGNESS TO PAY FOR IRRIGATION WATER
SYSTEM AS A MECHANISM FOR SUSTAINABLE WATERSHED
MANAGEMENT AND ENHANCED FOOD PRODUCTION IN
KERIO VALLEY BASIN KENYA
By
Jonah Kipsaat Kiprop, Job Lagat and Patience
Mshenga
9 th EGERTON UNIVERSITY INTERNATIONAL
CONFERENCE
25 th -27 th March 2015
EGERTON UNIVERSITY, KENYA
Kiprop et al .,2015

2. Outline
 Background Information
 Research issue/ Statement of the Problem
 Objectives of the study
 Study area
 Methodology
 Results
 Conclusions and Recommendations
Kiprop et al 2015

3. Background Information
 Water is a vital resource in enhancing agricultural
production in Kenya
 However, given the unreliable rains, irrigation is critical
in increasing and sustaining agricultural productivity
 With climate change projected to account for 20 percent
of the global increase in water scarcity (FAO-COAG,
2007). There is need to formulate policies that ensures
efficient allocation of water.
Kiprop et al 2015

4. Background Information
 Kenya is classified as a water deficit country with water
resources unevenly distributed in space and time
(ASDS, 2010-2020)
 Only 17 % of the land area is high potential thus
receiving adequate rainfall the remaining land is arid and
semi-arid and cannot support crop production without
irrigation
 The Government has acknowledged the relevance of
irrigated agriculture, in this regard it is a key component
of Agricultural Sector Development Strategy of 2010-
2020 towards achieving Vision 2030.
Kiprop et al 2015

5. Background Information
 Irrigation in Kenya is mainly carried out in irrigation
schemes with smallholder schemes accounting for 42%
while government managed schemes account for 18%
(RoK, 2010)
 Only a small fraction 1.8% of crop land in Kenya is
under irrigation while there lies a great potential of 1.3
million hectares (NIB, 2012) as illustrated in the figure
below.
Kiprop et al 2015

6. Irrigation potential in Kenyan basins
0
50,000
100,000
150,000
200,000
250,000
Tana Athi Lake Victoria Kerio Valley Ewaso Ngiro
AreainHa
Kenyan basins
Source: National Irrigation Board (NIB), 2012
Kiprop et al 2015

7. Policy efforts
 In the past a lot of efforts and funds were directed in
expanding smallholder irrigation schemes, however
most schemes failed due to lack of self-sustaining
systems
 The Draft Water Policy of 2010 emphasized the need for
enhancing the capacity of farmers to own, manage, and
finance irrigation schemes through formation of
Irrigation water users’ associations (IWUA’S)
 Water pricing as an economic instrument that has been
used worldwide to improve water allocation and to
enhance sustainability in management of irrigation
schemes (Bazza, 2002).
Kiprop et al 2015

8. Water Policy:
Treating water as an economic good
 Dublin Principles and IWRM—approach recommended
for MDGs
 2002 World Summit on Sustainable Development in
Johannesburg
 2003 Third World Water Forum
 2006 World Water Development Report
 Human Development Report 2006
Beyond scarcity: power, poverty and the global water
crisis
Kiprop et al 2015

9. What do we mean by ‘economic
value?’
A commodity has an economic value when people are willing to pay
for it, rather than go without it is a monetary measure of the
intensity of individual preferences (needs, wants, desires)
 Market goods
◦ Observed equilibrium market prices represent the willingness-to-
pay
 Non-market goods
◦ Benefits are based on individual values in the form of
willingness-to-pay (WTP) and their aggregation across all
affected individuals
◦ Costs are the value of the opportunities forgone because of the
commitment of resources to a project, or the willingness-to-pay
to avoid detrimental effects (damages).
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10. What do we mean by ‘economic
value?’
Water’s value is the willingness to pay
for water
It is observed when people make a
choice between different products
• How much will a household pay for
drinking water?
• How much will a farmer pay for
irrigation water?
• How much will a factory pay for
clean water?
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11. Most commonly used water valuation
techniques
Kiprop et al 2015
Frequency of
water valuation
studies Most common methods used
Residual value (and
variations)
Production function
CVM, programming models
Manufacturing Uncommon
Production function,
programming
Hydroelectric power Common
Programming models,
opportunity cost
Waste assimilation
services Common
Cost of prevention, Benefits
from damages averted
Agriculture Most common
application

12. Research Issue
 Elgeyo Marakwet County has a long history of
traditional furrow irrigation being practiced on the Kerio
basin dating back to 400 years ago (Kipkorir, 1983).
 Despite the traditional system bringing development in
the past, it was inefficient in water use (Chepkonga et
al., (2002).
 Currently the traditional systems are being upgraded to
modern systems, under this new arrangement water
users will pay a fee under the management of the
irrigation water users associations.
Kiprop et al 2015

13. Research Issue
 Being a new system little is documented on how farmers
will react to introduction of water pricing
.
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14. Transition from Traditional Irrigation system to
Modern system
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15. Research objectives
General objective
 To contribute to the sustainable management of
irrigation water in community managed smallholder
irrigation schemes, by establishing an effective water
pricing mechanism
Specific objectives
1. To determine the socio-economic factors which
influence the farmers’ willingness to pay for
irrigation water in the Kerio valley basin
2. To assess how much farmers’ are willing to pay for
irrigation water in the Kerio valley basin
Kiprop et al 2015

16. Conceptual framework
Institutional factors
• Access to credit
• Membership in IWUA
• Land tenure system
• Access to extension
service
Farm and
farmers
characteristics’
• Age of
farmer
• Education
level
• Farm size
• Occupation
• income
Attributes of the new system of
irrigation
•Minimal repair costs
•Irrigation land coverage
Outcome
•Improved management of water
resources.
•Reduced water conflicts
•Reduced water wastage
•Increased land under irrigation
•Enhanced food production
Not willing to pay
Farmers’ willingness to
pay for irrigation water
Farmers’ perceptions on
paying for irrigation water
Kiprop et al 2015

17. Methodology
Study area
 The study was undertaken in Elgeyo Marakwet County.
 216 smallholder irrigation farmers were sampled from
Arror irrigation scheme
 The major crops food grown are maize, mangoes bananas,
sorghum, millet and cowpeas. Cotton is grown cash crop
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18. Map of the Study Area
Source: www.wri .org
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19. Analytical framework
 Objective 1: To identify the factors which influence
farmers’ willingness to pay for irrigation water.
 The classical Probit model was used to identify the
socio-economic factors that influence farmers’ decision
to pay or not to pay for irrigation water.
 The outcome equation was;
Willingness to pay(Yi) = β0+ β1Agehh+ β2Edulevelhh+
β3Farmsize+ β4Croptype+ β5Perc-mai+ β6Distmkt+
β7Famlysize+ β8Tlu-own+ β9Crd-acc+ β10Ext-ctc+
β11Income-irr+ β12Tot-income+ β13Traing+ β14Expr-
irr+ β15Memb-iwua+ β16Prox-water+ β17Perc_mai+ ε
Kiprop et al 2015

20. Analytical framework
 Objective 2: To determine the farmers’ mean willingness
to pay for irrigation water in the Kerio Valley basin.
 The double bounded contingent valuation method was
used to value the water resource since there is no market
for irrigation water in the area.
 Once the farmer made the choice to pay, the next decision
was to determine the amount of payment (intensity) in
Kenyan Shillings.
Kiprop et al 2015

21.  If the respondent replies “no’’ for the first bid, then
further discussions on the payment are terminated.
 On the other hand if the respondent’s choice is ‘’yes’’
then a second question is posed with a starting bid value.
If the payment choice for Kshs, is ‘’yes’’ then the
respondent will face another level of bid choice, which
would be higher or lower amount, respectively.
 This second amount (bid) is based on the response of
the first bid (if the response for the first is yes, then the
following bid will be double the first one and half if
otherwise).
Kiprop et al 2015

22.  The probabilities of the outcomes can be represented by
p (yy); p (nn); P (yn); and p (ny) for “yes”, “yes’’, “no”,
“no’’, “yes”, “no’’ and “no”, “yes’ ’outcomes
respectively. Following Hanemann et al. (1991), these
likelihoods can be represented mathematically as;
 The probability of “ no, no” outcome is represented as:
Pnn(Bi
1,Bi
1) = P (Bi
L >Max.WTP and Bi
L >Max.WTP) = G(Bi
L,ɵ)
 The probability of “yes, yes” will be:
Pyy(Bi
1,Bi
U) = P (Bi
L >Max.WTP and Bi
U >Max.WTP) = G(Bi
U,ɵ)
 When a “yes” is followed by “no” we have:
Pyn(Bi
1,Bi
U) = P (Bi
L <Max.WTP ≤ Bi
U ) =G(Bi
U, ɵ) − G(Bi
L, ɵ)
Kiprop et al 2015

23.  When a no is followed by a yes response the probability is :
Pny(Bi
I,Bi
L) = P (Bi
I >Max.WTP≥ Bi
L ) =G(Bi
I, ɵ) − G(Bi
L, ɵ)
 With a sample of N observations where B is the various bid
values the outcome equation is;
 L(ɵ) = Ʃ di
yy .Pyy (Bi
1,Bi
U) +di
nn.Pnn(Bi
I,Bi
L) + di
yn .Pyn
(Bi
1,Bi
U) +di
ny.Pny(Bi
I,Bi
L)
Kiprop et al 2015

24. Variable Variable
Code
Types of
variable
Unit of Measurement of the Expected
sign
Dependent variables
Willingness to pay for irrigation water WTP Dummy 1 for those willing to participate
and 0 other wise
Independent variables
Education level of household head EDULHH Continuous Years -
Age of household head AGEHH Continuous Years -
Type of crop Grown CROP-TYP Dummy 1 if cash crops are produced,0
otherwise
+
Perception about operation and maintenance PERC-MAI Dummy 1 if perceived,0 otherwise +
Distance from the market DIST-MKT Continuous Kilometre -
Household family size FAMSIZE Continuous Number of persons in a
household
+/-
Livestock ownership TLU-OWN Continuous Number of livestock owned +/-
Access to credit service CRD-ACC Dummy 1 if accesibles,0 otherwise +/-
Access or contact with extension service EXT-CTC Dummy 1 if accessible , 0 otherwise +
Income from irrigated farm INCOME-
IRR
Continuous Kenyan Shillings +
Access to training TRAING Dummy 1 Trained,0 otherwise +
Membership in irrigation water users
association
MEMB-
IWUA
Dummy 1 Member, 0 otherwise +
Proximity to water source PROX-WS Continuous Kilometre -
Perception and observation about maintenance
problem
PERC_MAI Dummy 1 if perceived, 0 otherwise +/-
Description of variables and the expected Signs to be used in the models
Kiprop et al 2015

25. Results
 Approximately 91.4% of the smallholder
farmers were willing to pay for irrigation water
with a mean Willingness to pay of Ksh 938 per
production season.
 This represents about 9.6% of the average total
farm income.
Kiprop et al 2015

26. Factors Influencing farmers decision on WTP
Variables Coefficient Std. Err. z
Education level 2.88 1.34 2.14**
Age of farmer -0.017 0.023 -0.74
Participation in construction 1.50 0.75 2.01**
Household size 0.25 0.18 1.42
Gender of household head -0.74 0.71 -1.03
Distance to the market -0.35 0.12 -2.76
Total livestock ownership 0.008 0.015 0.54
Access to credit service -0.064 0.90 -0.07
Access to extension service -1.64 0.83 -1.97**
Total income from irrigated farm 5.80 1.53 3.79**
Access to agricultural training 1.88 0.71 2.62
Membership in (IWUA) 1.72 0.81 2.10**
Distance to water source -0.352 0.12 -2.88*
Constant 4.18 1.62 2.58*
N 216
LR χ2 95.10
Prob> χ2 0.000
Pseudo R2 0.7707
Log likelihood -14.143*, **, *** significant at 10, 5 and 1 percent level, respectively
Kiprop et al 2015

27. Factors influencing farmers mean willingness to pay for
irrigation water
Variable Coefficient Std. Err. z
Age of farmer -30.27558 6.061803 -4.99**
Household size 109.3838 33.70524 3.25*
Membership in IWUA 76.38428 238.9641 0.32
Access to credit 2.598333 174.4956 0.01
Access to extension -423.3809 230.2513 -1.84
Access to training -136.5829 186.0542 -0.73
Participation in construction 282.9909 220.926 1.28
Distance to water source -97.71583 38.67595 -2.53**
Distance to the market -68.43047 28.59172 -2.39
Total livestock owned 0 .0151607 2.556768 0.01
Income from irrigation 53064 .0020247 2.62*
Constant 938.4346 560.7905 1.67***
Number of observations 197
F(14, 120) 15.78
Prob >F 0.000
R-squared 0.6461
Adjusted R-squared 0.6081
*, **, *** significant at 10, 5 and 1 percent level, respectively
Kiprop et al 2015

28. Conclusions and Recommendations
 More capacity building initiatives such
as training and field days should be
undertaken to enhance the farmers’
willingness to pay
 Establishing a feasible water
charging system in the schemes such
as the volumetric basis of water
charging will be helpful.
Kiprop et al 2015

29. Conclusions and Recommendations
 The water users associations should be
strengthened through training of technical
staff such as plumbers who will ensure
water systems are properly maintained
 Adequate extension support should be
delivered more specifically on irrigation
farming so that farmers would be able to
make efficient use of their irrigated land
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30. Conclusions and Recommendations
 Implementing an irrigation water
management system that ensures
equitable water distribution and
effective enforcement of existing
rules and regulations, would not only
enhance the farmers’ willingness to
pay but also the amount they would
commit
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31. Acknowledgements
 African Economic Research Consortium
(AERC) for their funding the research
through the CMMAE program
 Department of Agricultural Economics
and Agribusiness Management Egerton
University
 KVDA field staff
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32. THANK YOU
Kiprop et al 2015