Extent of adoption of mobile phone applications by smallholder dairy farmers in Tharaka Nithi County, Kenya

Figures & data

Figure 1. Map of the study area.

Table 1. Definition of explanatory variables

Variable	Definition of variable	Hypothesis
Dependent Variable
Adoption	Decision to adopt a mobile phone application (Yes =1, otherwise 0)
Extent of use	The number of mobile applications the farmer uses
Independent Variables
Age	Age recorded in the number of years (continuous variable)	-/+
Education	Years spent in school taken as cumulative from lowest to highest level (continuous variable)	+
Sex	Recorded as a dummy variable, representing the sex of the farmer (1= Male, 0 = female).	-/+
Occupation	Farmers key occupation (dummy variable 1= Farming, 0=Otherwise)	-/+
Income	Total household annual income in Ksh (continuous variable), estimated from all sources of income for the household	+
Farm size	Farm size in Ha (continuous variable), collected from title deeds.	-/+
Access to credit	Access to formal credit (financial and/or inputs) a dummy variable (1= farmer has gotten credit in the last 12 months, 0=Otherwise)	+
Distance to the main road	Distance from the farm location to the nearest tarmac road (km) (continuous variable), calculated using QGIS	-
Distance to the market	Distance from the farm location to the nearest market (km) (continuous variable), calculated using QGIS	-
Farmer organisation	Farmer belongs to a farmer’s organization (dummy variable 1= Yes, 0=Otherwise)	+
Sources of information about mobile applications
Public extension	1= farmer got information from the government extension officer, 0 otherwise	+
Private extension	1= farmer got information from the private extension service providers, 0 otherwise	+
Researcher	1= farmer got information from officers from the National Research Organizations and the Universities, 0 otherwise	+
Social media	1= farmer got information from social media, 0 otherwise	+
Ease of using an app	Perception on the ease of using mobile applications a dummy variable (1= mobile applications are easy to navigate 0=Otherwise)	+
Cost of using an app	Perception on affordability of mobile applications a dummy variable (1= affordable 0=Otherwise)	+

Table 2. Socioeconomic characteristics of the sampled households (continuous variables)

Characteristic	Mean	Std. dev.
Age of the respondent in years	46	13
Years in school	12	3
Household size	4	1
Total household annual income (Ksh)	161,149	97,598
Distance from the farm to the tarmac road (km)	5.29	2.83
Distance from the farm to the nearest market (Km)	2.40	1.69
Farm size in Ha	0.71	0.58
Number of cows kept by respondents	2	1
Number of contacts with extension providers for last 12 months	2	1
Total number of farmers N = 427

Table 3. Socioeconomic characteristics of the sampled households (dummy variables)

Characteristic	Yes	No
Use of mobile applications	51%	49%
Gender (Yes =Male; No = Female)	46%	54%
Respondent belong to a farmer organization	86%	14%
Contact with extension service providers in the last 12 months	56%	44%
Respondent awareness of using mobile phone to access agric infor	77%	23%
Access to internet	85%	15%
Access to formal credit in the last 12 months	60%	40%
Total number of farmers N = 427

Figure 2. Common mobile applications used to access agricultural information by farmers in Tharaka Nithi, Kenya.

Table 4. Differences in characteristics of users versus non-users of mobile applications (dummy variables)

Characteristic	Users		Non-users	Pearson Chi-Square
	N=216		N=211
Gender	Male	54%	38%	10.685***
	Female	46%	62%
Respondents belong to a farmer organization	Yes	89%	83%	3.208
	No	11%	17%
Access to formal credit for last 12 months	Yes	69%	49%	17.991***
	No	31%	51%
Access to internet	Yes	100%	69%	79.916***
	No	0%	31%
Awareness of using mobile phone to access Agric Information	Yes	100%	53%	133.675***
	No	0%	47%
Contact with extension service provider	Yes	67%	45%	19.432***
	No	33%	55%

Significance of mean difference at ***1%.

Table 5. Differences in characteristics of users versus non-users of mobile applications (continuous variables)

Characteristic	Users	Non-users	t –test
Age of the respondent in years	43	49	−4.785***
Years in school	12	11	5.397***
Household size	4	3	1.864
Total household annual income (Ksh)	179,368	142,498	3.70***
Distance from the farm to the main road (Km)	5.15	5.43	−1.048
Distance from the farm to the nearest market (Km)	1.89	2.93	−6.732***
Farm size in Ha	0.79	0.63	2.936
Number of cows kept by respondents	2	2	−0.372
Number of contacts with extension providers in last 12 months	2	1	4.431***
Total number of farmers (N)	216	211

Significance of mean difference is at ***1%.

Table 6. Log likelihood Ratio test for Tobit and Probit + Truncated models

	Probit	Truncated	Tobit
Log likelihood	−172.27	−193.63	−416.7
No. of observations	427	216	427
Test statistics: Г= 101.6 > χ^2 0.05,16 = 26.29

Figure 3. Area under receiver operating Characteristic curve test.

Table

No. of observations	427
Number of covariate patterns	427
Pearson χ^2 (410)	391.11
Prob > χ^2 = 0.7411

Table 7. Double hurdle results on factors influencing the decision to adopt mobile phone applications

	Probit model		Truncated model
Variable	Marginal Effects	Std. Error	Coefficient	Std. Error
Sex of household head	0.08**	0.04	−0.17	0.10
Age of household head	−0.003*	0.002	0.002	0.005
Distance to main road	0.01	0.01	0.005	0.02
Distance to the market	−0.07***	0.01	0.01	0.04
Education of Household head	0.02***	0.01	−0.01	0.02
Occupation	0.05**	0.02	0.01	0.05
Household Income	−0.02	0.03	0.05	0.05
Farm size	0.04	0.04	0.20**	0.09
Group membership	0.01	0.05	0.21	0.18
Access to Public extension	0.18***	0.07	0.18	0.13
Access to Researcher	0.33***	0.10	0.54***	0.17
Access to Private extension	0.21***	0.05	0.32***	0.12
Access to social media	0.18***	0.05	0.31***	0.12
Access to Credit access	0.10***	0.04	0.13	0.12
Ease of using an app	0.21***	0.06	0.02	0.23
Cost of using an app	0.17***	0.04	−0.13	0.14

***significant at 1%, **significant at 5%, *significant at 10%.

Data availability statement

Data is available upon a reasonable request.