Assessment of willingness-to-pay for Aflasafe KE01, a native biological control product for aflatoxin management in Kenya

Figures & data

Figure 1. Distribution of sampled households (HH) in Tana River, Makueni, Machakos and Kitui Counties of Kenya

Table 1. Description of hypothesised independent variables used to assess farmers’ willingness to pay for Aflasafe KE01 in Kenya

Variable name	Symbol	Description of the variable	Expected sign
Access to crop extension	Extcon	1 = accessed within last normal year, 0 = No access	+
Credit access	CreditAcc	1 = access credit within last one year, 0 = no access	+
Awareness of bio-fungicide	AwareBio	Dummy 1 = aware, 0 = not aware	+
Experienced Aflatoxin contamination	AflaConta.	Dummy 1 = Yes, 0 = No	+
Experienced maize loss to diseases	Disease	Dummy 1 = Yes, 0 = No	+
Household size	Hhsiz	Number of the household members	+
Contract agreement of sale	CntrtAgrmt	Dummy 1 = Have a contract agreement 0 = No contract	+
Years of main livelihood activity	Experience	Number of years of practicing main livelihood activity	+
Income	Hhinc	Natural log of household income	+
Agricultural group membership	MbrAgric	Dummy for membership to group 1 = Yes, 0 = No	+
Maize production	Prodn	The quantity of maize harvested in 90 kg bags last season	+
Age (Years)	Age	Age of the household head/decision-maker	+
Area under cultivation	AreaCult	Acres under cultivation	+
Land tenure	Tenure	1 = Formal ownership of land, 0 = No formal ownership	+
Distance to market	DistMkt	Distance to the local market in km or miles or minutes???	-
Education	HEDUC	Number of years of schooling completed	+
Initial bid amount	Initamt	1 = if the household said yes to the initial amount; 0 = Otherwise	-
Gender	Gender	Gender of the household head 1 = Male, 0 = Otherwise	±
Perception of product effectiveness	PercEffective	Whether a farmer perceives the product to be effective 1 = Yes, 0 = Otherwise	-
Main occupation of the household head	Occup	1 = On- farm activities 0 = Off-farm activities	+
Bura	Bura	The respondent residence 1 = Bura, 0 = Otherwise (Hola)	±

Figure 2. Kenyan farmers` willingness to pay (WTP) for Aflasafe KE01 compared to equivalent cost of Nigeria’s Aflasafe

Table 2. Descriptive statistics of explanatory variables hypothesised to influence maximum willingness to pay for Aflasafe KE01

	Tana River County, n = 32			Lower eastern (trial), n = 134			Lower eastern (non-trial), n = 314
Variables^2	Min	Max	Mean (SD)	Min	Max	Mean (SD)	Min	Max	Mean (SD)
Extcon	0	1	0.50 (0.51)	0	1	0.65 (0.48)	0	1	0.40 (0.49)
CreditAcc	0	1	0.69 (0.47)	0	1	0.10 (0.30)	0	1	0.05 (0.21)
Hhsiz	1	18	6 (3.81)	1	15	5 (2.41)	1	17	5.11 (2.30)
AwareBio	0	1	0.97 (0.18)	0	1	0.87 (0.34)	0	1	0.06 (0.23)
CntrtAgrmt	0	1	0.97 (0.18)	0	1	0.08 (0.28)	0	1	0.04 (0.19)
Aflaconta	0	1	0.41 (0.50)	0	1	0.93 (0.26)	0	1	0.28 (0.45)
DistMkt	1	4	1.1 (1.15)	1	15	2.21 (2.65)	1	15	3.17 (1.84)
MbrAgric	0	1	0.66 (0.48)	0	1	0.49 (0.50)	0	1	0.24 (0.43)
Hhinc(ln)	9.21	11.61	9.70 (0.72)	9.21	11.74	9.88 (0.82)	9.21	11.73	9.73 (0.734)
Prodn	3	80	21.43 (14.64)	0.8	95	16.46(15.41)	0.5	75	11.05(11.52)
HEDUC	1	18	6.44 (4.33)	1	19	9.9 (4.22)	1	19	9.18 (4.14)
Disease	0	1	0.56 (0.50)	0	1	0.31 (0.46)	0	1	0.36 (0.48)
Gender	0	1	0.56 (0.50)	0	1	0.37 (0.48)	0	1	0.40 (0.49)
Age	23	74	44.1 (15.29)	24	87	51.4 (14.15)	23	92	52.03 (15.78)
Experience	2	40	10 (10.79)	4	54	22 (12.10)	1	72	21 (14.68)
PercEffective	0	1	0.91(0.30)	0	1	0.79(0.41)	0	1	0.55(0.50)
Occup	0	1	0.88 (0.34)	0	1	0.84 (0.36)	0	1	0.85 (0.36)
Bura	0	1	0.59 (0.50)
AreaCult	1.5	6	2.95 (0.97)	1	40	4.57 (4.34)	0.38	20	3.95 (3.31)
Tenure				0	1	0.41 (0.49)	0	1	0.35 (0.48)
Initamt	0	1	0.41 (0.50)	0	1	0.55 (0.50)	0	1	0.55 (0.50)

²For explanation of variables, see Table 1. SD- Standard deviation

Table 3. Test for heteroskedasticity and Ramsey test for willingness to pay of Aflasafe KE01 in Kenya

	Tana River County farmers	Lower eastern trial farmers	Lower eastern non-trial farmers
Breusch-Pagan/Cook-Weisberg test for heteroskedasticity	${\chi }^2$(1) = 0.66 Prob >${\chi }^2$= 0.4155	${\chi }^2$(1) = 1.14 Prob >${\chi }^2$= 0.2850	${\chi }^2$(1) = 4.53 Prob>${\chi }^2$= 0.2333
Ramsey RESET test	F (3,10) = 1.69 Prob > F = 0.2309	F (3,108) = 1.98 Prob >F = 0.1215	F (3,293) = 2.05 Prob >F = 0.1076

Table 4. Factors influencing willingness to pay for Aflasafe KE01 for the three farmers’ categories from Tana River and lower Eastern regions in Kenya

	OLS Regression model estimates
	Dependent variable: Max WTP
		Tana River County		Lower eastern (Trial area)		Lower eastern (non-trial area)
Variables^3	Expected sign	Coeff.	P-value	Coeff.	P-value	Coeff.	P-value
Constant		107.77	0.217	3.27	0.000***	4.02	0.000***
Extcon	+	30.51	0.053*	0.06	0.395	0.01	0.851
CreditAcc	+	36.94	0.013**	0.21	0.064*	0.21	0.020**
Hhsiz	+	−3.10	0.100*	−0.02	0.235	−0.03	0.001***
AwareBio	+			0.11	0.363	0.19	0.023**
CntrtAgrmt	+			0.16	0.156	0.26	0.018**
Aflaconta	+	−2.36	0.924
DistMkt	-	3.32	0.585	−0.003	0.792	−0.02	0.071*
PercEffective	-	−15.56	0.064*
MbrAgric	+	−8.67	0.545	0.10	0.179	0.02	0.698
Hhinc	+	−1.25	0.881	0.08	0.061*	0.05	0.067*
Prodn	+			0.002	0.363	0.0003	0.868
HEDUC	+	−0.05	0.974	−0.004	0.643	−0.005	0.316
Disease	+	8.96	0.515
Gender	±	−9.50	0.408	−0.10	0.183	0.07	0.089*
Experience	+	−0.89	0.097*	−0.01	0.060*	−0.002	0.181
Age	+			0.01	0.028**
Occup	+	−0.52	0.977	−0.004.	0.959	0.061	0.260
Bura	±	20.98	0.086*
AreaCult	+	−2.43	0.688	0.01	0.211	0.0003	0.983
Tenure	+			−0.06	0.435	0.023	0.576
Initamt	-	79.31	0.000***	0.973	0.000***	0.898	0.000***
No. of obs.			30		129		313
F(16,13)			10.30	F(17,111)	15.77	F(16,296)	43.03
Prob.>F			0.0001		0.000		0.000
Adjusted R-squared			0.8369		0.6623		0.6831
Root MSE			21.01		0.34886		0.3231

³For explanation of variables, see Table 1.

*, ** and *** implies statistically significant at 10%, 5% and 1% respectively