This presentation was made by Dr. Mathieu Ouédraogo, Participatory Action Research specialist/CCAFS West Africa at the WASCAL Science Symposium, 19-21 June 2018, Tang Palace Hotel, Accra, Ghana

1. Evidence from Cowpea and Sesame Producers in
Northern Burkina Faso
Farmers’ Willingness to Pay
for Climate Information
Services (CIS):
WASCAL Science Symposium – Accra, 19-21 June 2018
Mathieu Ouédraogo, Robert B. Zougmoré,
Samuel T. Partey et al.

2. Outline
Background
• Climate variability in the Sahel
• Climate information services
Objectives of the study
Study context and methods
Main findings
• Appropriate channels for climate information dissemination
• Willingness-to-Pay for Climate Information Services
• Determinants of the Willingness-to-Pay for CIS
• Predicted WTP and Estimation of Consumer Surplus of CIS
Conclusion and recommendations

3. Climate variability in the Sahel: Facts !
GHCN Series: 12-20N, 18W-30E

4. Climate variability and agriculture
in West Africa
• Climate variability is the principal source of
fluctuations in global food production in the Sahel.
• The increase of rainfall variability and extreme
climate events as consequence of CC will have
impacts on agricultural production and food
security in West Africa.
• Need for climate risk management tools, such as
climate information services (CIS).
• CCAFS has been using its Climate-Smart Village
approach to provide CIS to farmers in WA (2011).

5. • Approach where CCAFS in
partnership with rural communities
and other stakeholders (NARES,
NGOs, local authorities…), tests &
validates in an integrated manner,
several agricultural interventions.
• To boost farmers’ ability to adapt to
CC, manage risks and build
resilience.
AR4D Climat-Smart Village approach
Scaling up
• Policy
• Private sector
• Mainstream successes
via major initiatives
• Learning sites
• Multiple partners
• Capacity building

6. Using climate information for early warming
Maturity/end season
Ten-day forecast
 optimum
harvesting period
 rain during dry
season
Nowcasting
 flooding saving life (thunder)
Daily forecast
 use of fertilizer / pesticide
Ten-day forecast
 weeding, field work
Updating seasonal forecast
 second cropping
Seasonal forecast
 crop variety
 varieties
Onset forecast
 farm
preparation
 optimum
planting
Before During cropping season

7. Objective of the study
• This study aimed to assess the willingness of
cowpea and sesame farmers to pay for CIS as
an entry point to bringing CIS to scale and
sustaining its use by farmers in Burkina Faso.

8. Study context and methods
• Study area :
 CSV site of Yatenga, North of Burkina Faso
 Block of 51 villages
 Annual rainfall: 600 mm

9. Study context and methods
The seasonal forecasts (workshops) The ten-day forecasts and the
daily CI (radio shows).
Climate information communication

10. Study context and methods
• Data collection: Farm surveys on 170 farmers from
17 villages (11 experimental and 6 control villages)
in 2014.
• Contingent valuation method (CVM) to determine
the willingness of farmers to pay (WTP) for CIS.
• Tobit model to identify the key determinants of
WTP for CIS.

11. Determinants of WTP for CIS
With : WTP*i = latent or inobservable value of the WTP for CI ; yi =
WTP for CI ; xi = is vector of independent variables that are
hypothesized to influence the WTP for CI and β = is unknown
parameter vector to be estimated.
Tobit model (1958)
The marginal effect of an explanatory variable on the
expected value of the WTP is :
    zbydenotedis
x
wherezF
x
WTPE ii
i
i
i













00
0
*
**
*
i
ii
i
iii
WTPif
WTPifWTP
WTP
xWTP 

12. Appropriate channels for climate
information dissemination
Appropriate channels for climate information dissemination (%)
69
4
1 2 2
1
22
0
10
20
30
40
50
60
70
80
Radio Television Workshops
(face to face
meetings)
Mobile phone Extension agent Farmer’
organisation
No response

13. Are farmers willing to pay for CIS ?
63% of respondents
were willing to pay for
CIS such as seasonal
climate forecast
(53%), decadal climate
information (33%),
daily climate
information (53%) and
agro-advisories (39%).
3706 (53%)
1113 (33%)
1923 (53%)
1674 (39%)
0 1000 2000 3000 4000
Seasonal climate
forecast
Decadal climate
information
Daily climate
information
Agro-advisories
WTB (%) and WTP (F CFA) for CIS

14. Determinants of WTP for CIS
Variables Seasonal Climate Forecast Decadal Climate Information
Coefficient Marginal Effect Coefficient Marginal Effect
Sex male
5824.99 *** 2503.63 *** 3745.14 ** 842.18 **
(2133.30) (831.88) (1815.41) (370.18)
Educated
−33.79 −15.83 −3657.87 * −757.16**
(2232.58) (1045.00) (1972.73) (340.46)
Age
−121.42 −56.93 −216.19 *** −53.92 ***
(86.56) (40.53) (75.03) (18.94)
Household size
91.51 42.91 54.34 13.55
(167.17) (78.47) (139.78) (34.88)
Farm size
−104.25 −48.88 −82.08 −20.47
(150.53) (70.74) (132.65) (33.11)
Indigenous forecast
1468.60 686.82 1866.44 463.45
(1856.84) (865.35) (1568.25) (387.31)
Awareness to CIS
8414.33 *** 3563.83 *** 4844.48 *** 1089.27 ***
(2039.17) (772.67) (1722.45) (354.30)
Stone bunds
−1520.31 −690.21 −4809.52 ** −988.98 ***
(2020.72) (889.33) (1922.07) (332.57)
Organic manure
4894.90 ** 2207.61 *** 1639.68 398.89
(1908.99) (829.02) (1616.18) (383.16)
Secondary activity –livestock
1363.31 642.66 1717.26 434.98
(1735.66) (823.46) (1464.70) (377.10)
Market oriented
833.51 396.66 −1352.27 −321.40
(1943.45) (937.42) (1656.94) (377.31)
Constant
−9148.61 * −1370.96
(4658.70) (3917.38)
Sigma
9681.28 *** 7172.08 ***
(776.08) (730.45)
Number of obs 169 169
Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

15. Determinants of WTP for CIS
Variables
Daily Climate Information Agro-Met Advisories
Coefficient Marginal Effect Coefficient Marginal Effect
Sex male
5471.56 *** 2023.92 *** 4469.12 ** 1305.66 ***
(1474.08) (481.72) (1845.78) (487.20)
Educated
−2953.05 * −1094.42 ** −1902.28 −568.35
(1542.95) (500.75) (1952.48) (536.76)
Age
−178.94 *** −75.34 *** −114.23 -36.99
(59.38) (25.13) (75.87) (24.61)
Household size
42.36 17.83 153.70 49.78
(112.42) (47.35) (141.65) (45.82)
Farm size
−48.78 −20.54 −270.14 −87.49
(100.85) (42.50) (172.71) (55.67)
Indigenous forecast
141.22 59.43 2610.37 840.90
(1238.55) (520.91) (1590.74) (509.70)
Awareness to CIS
5745.50 *** 2167.75 *** 4696.45 *** 1394.19 ***
(1389.97) (467.56) (1701.12) (464.40)
Stone bunds
−1072.17 −434.91 −2417.99 −720.70
(1370.20) (536.11) (1774.13) (488.09)
Organic manure
3074.98 ** 1245.08 ** 4449.98 *** 1368.42 ***
(1290.98) (503.29) (1648.35) (485.29)
Secondary activity –livestock
862.08 364.99 359.84 116.81
(1178.38) (500.97) (1488.09) (483.99)
Market oriented
1095.09 475.99 −955.06 −300.77
(1301.84) (582.17) (1675.67) (514.12)
Constant −2622.36 −7481.36 *
(3138.12) (4090.67)
Sigma 6463.43 *** 7700.16 ***
(501.84) (719.24)
Number of obs 169 169
Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

16. Value for climate information
services at the CCAFS CSV site of
Yatenga in 2014
CIS
Total
Households
at the CSV
Site
%
Households
Willing to
Pay for CIS
Expected
Number of
Households
Willing to
Pay CIS
Predicted
Value of
the WTP
Per Year
Aggregated
Value (XOF)
*
Seasonal forecast 12,662 52.66 6668 3496 23,312,723
Decadal information 12,662 32.54 4121 1066 4,394,357
Daily information 12,662 53.25 6743 1985 13,388,368
Agro-advisories 12,662 39.05 4945 1628 8,050,094
* 1 EUR = 655.957 XOF.

17. Conclusion & recommendations
• The study assessed farmers’ willingness-to-pay
(WTP) for CIS and analyzed its determinants.
• While there was a general increased demand for
CI, the magnitude of such demand depended on
the type of CI (Seasonal climate forecasts and
daily weather information were most demanded
(53%)).
• The WTP for CIS also depended on farmers’
characteristics (factors such as gender, age,
education, awareness of climate information, use
of SWC techniques and organic manure were
particularly more influential in determining farmers’
WTP for CIS).

18. Conclusion & recommendations
• The results of this study provided information on
the type of CIS to prioritize in the study area, most
appropriate dissemination channels and the cost
implications that must be mainstreamed into future
scaling up to achieve large-scale adoption by
farmers.
• The outcomes of this study should support policy
makers to better design an efficient mechanism
for the dissemination of climate information to
improve the adaptive capacity of farmers to
climate risks in Burkina Faso.

19. Collaborators
• CCAFS / ICRISAT Mali
• INERA –Burkina Faso
• Direction de la Météorologie
Nationale-Burkina Faso
• PROFIL (IFAD funded project)
Ouédraogo M, Barry S, Zougmoré B.R, Partey T.S., Somé L, Baki G, 2018.
Farmers’Willingness to Pay for Climate Information Services: Evidence from
Cowpea and Sesame Producers in Northern Burkina Faso. Sustainability 2018,
10, 611; doi:10.3390/su10030611, www.mdpi.com/journal/sustainability