Determinants of the intensity of adoption of climate-smart horticulture practices in Taita-Taveta County, Kenya

Figures & data

Figure 1. A map of the research sites in Taita-Taveta County.

Figure 2. A conceptual framework on the potential determinants of adoption of CSH practices.

Table 1. Variables used in the analysis.

Variable	Measurement
Gender of the farmer	1= Man, 0= Woman
Household size	Number of members
Farmer’s education level	Years of formal education
Farming experience	Years of farming
Farm size under crop	Acres
Trust on information received	1 = Yes, 0 = No
Mobile phone use in CSH	1 = Yes, 0 = No
Group membership (formal and informal farmer group)	1 = In a farmer group, 0= Not in a farmer group
Access to agricultural extension service	1 = Yes, 0 = No
Access to credit (digital and formal credit)	1 = Yes, 0 = No
Climate change awareness	1 = Aware, 0 = Not aware
CSH awareness	1 = Aware, 0 = Not aware
Distance from farm to commercial bank	Kilometres

Table 2. Characteristics of different types of climate-smart horticulture farmers.

Variables	Green grams (n = 259)	Tomatoes (n = 115)	Both green grams and tomatoes (n = 29)	Pooled (n = 403)
Gender (men =1)	0.47	0.84	0.79	0.60
Household size (number of dependants)	5.47	5.24	4.69	5.35
Education (years)	7.63	9.73	8.83	8.32
Farming experience (years)	9.05	10.61	7.86	9.41
Farm size (acres)	1.47	0.99	2.23	1.39
Trust on agricultural information received through mobile phone	0.50	0.53	0.55	0.51
Mobile phone use on CSH	0.47	0.70	0.76	0.56
Agricultural group membership (yes = 1)	0.49	0.27	0.48	0.43
Access to agricultural extension services (yes = 1)	0.57	0.73	0.72	0.63
Access to credit (yes = 1)	0.10	0.10	0.14	0.10
Climate change awareness	0.92	0.94	0.93	0.92
CSH awareness	0.65	0.75	0.86	0.69
Distance from farm to commercial bank (km)	19.23	15.84	28.99	18.97

Figure 3. Crop-specific adoption of climate-smart horticulture practices.

Table 3. Differences in adoption of CSH practices in the study locations.

Climate-smart horticulture practice	Mwatate (n = 122)	Taveta (n = 222)	Wundanyi (n = 59)
Planting well-adapted seed varieties	0.61^b	0.40^c	0.70^a
Matching planting dates with weather information received	0.24	0.22	0.32
Crop rotation	0.68^b	0.68^b	0.90^a
Use of cover crops	0.31^b	0.26^c	0.63^a
Efficient use of inorganic fertilizer	0.17^b	0.14^c	0.27^a
Terracing	0.56^b	0.45^c	0.85^a
Agroforestry	0.45	0.45	0.58
Use of live barriers	0.39^b	0.09^c	0.78^a
Mixed farming	0.48	0.45	0.56
Crop insurance	0.03	0.02	0.03
Crop diversification	0.49	0.48	0.53
Use of organic fertilizer and compost manure	0.56^b	0.32c	0.78^a
Mulching	0.43^b	0.33^c	0.59^a
Minimum tillage	0.21	0.30	0.20
Use of farm ponds	0.19^c	0.21^b	0.41^a
Integrated pest management (IPM)	0.11^c	0.18^b	0.29^a
Contour cultivation	0.30^b	0.27^c	0.58^a

Note: Superscripts ^{a, b, c} represent statistically significant differences (at 10% or better) in CSH practices between the three locations in descending order.

Figure 4. Adoption of climate-smart horticulture practices among farmers in Taita-Taveta County.

Table 4. Negative binomial regression results on determinants of the number of CSH practices adopted in Taita-Taveta County.

Variable	Tomato farmers (n = 115)		Green gram farmers (n = 259)		Pooled (n = 403)
	Coefficient	Std. Error	Coefficient	Std. Error	Co-efficient	Std. Error
Dependent variable Number of CSH practices (count)
Independent variables
Gender of the farmer (men = 1)	−0.076	0.101	0.150**	0.062	0.184***	0.047
Household size (count)	0.009	0.018	0.014	0.013	0.014	0.001
Farmer’s education level (years)	0.022*	0.013	0.020**	0.009	0.035***	0.006
Farming experience (years)	0.008*	0.005	0.006	0.004	0.010***	0.003
Farm size under crop (acres)	0.016	0.030	−0.008	0.023	−0.044***	0.015
Trust on information received (yes = 1)	0.061	0.069	0.253***	0.069	0.115***	0.045
Mobile phone use in CSH (yes = 1)	0.108	0.080	0.206***	0.066	0.241***	0.048
Group membership (formal and informal farmer group) (yes = 1)	0.082	0.078	0.248***	0.083	0.045	0.047
Access to agricultural extension service (yes = 1)	−0.065	0.096	0.225**	0.094	0.170***	0.060
Access to credit (digital and formal credit) (yes = 1)	−0.051	0.119	0.026	0.099	−0.029	0.070
Climate change awareness (yes = 1)	0.119	0.187	−0.356***	0.116	−0.291***	0.090
CSH awareness (yes = 1)	0.240**	0.103	0.705***	0.087	0.544***	0.063
Distance from farm to commercial bank (km)	−0.005	0.003	0.008***	0.002	0.003**	0.001
	Constant = 1.512*** Log likelihood = −256.08 Pseudo-R^2 = 0.0584 Prob > chi^2 = 0.0026		Constant = 0.335* Log likelihood = −562.64 Pseudo-R^2 = 0.1514 Prob > chi^2 = 0.0000		Constant = 0.725*** Log likelihood = −956.99 Pseudo-R^2 = 0.1150 Prob > chi^2 = 0.0000

Note: *, ** and *** denote statistical significance at 10%, 5% and 1% levels, respectively.