Uptake of climate-smart agricultural practices among smallholder sorghum farmers in Busia County, Kenya

Figures & data

Table 1. Sampling size distribution per sub-county

Sub-county	Number of farming households	Proportionate distribution formula	Sample size
Teso North	17,182	17,182 *423/81,350	90
Teso South	26,895	26,895*423/81,350	140
Matayos	16,539	16,539*423/81,350	85
Samia	20,734	20,734*423/81,350	108
Total	81,350	Total	423

Source: Busia County agricultural offices

Table 2. Description of variables and descriptive statistics

Variables		Mean
Continuous variables	Variable description and their measurements
Age	Age of the decision-maker^a in years	48.07
Education_level	Years of education of the decision-maker	8.25
Farm_Experience	Decision-maker’s farming experience in years	21.36
Hhold_size	Number of household members	6.56
Prop_incomeallocatedfarm	Proportion of income allocated to farming	0.15
Prop_underfarmingAcres	Proportion of land under farming (Acres)	0.68
Distance_MarketCrop	Distance from home to the market (in km ^b)	2.53
extensionvisits	Frequency of extension visits in a year	1.90
Agrometinfor	Agrometeorological information count in a year	1.10
Training	Training counts attended in the last 12 months	2.15
Categorical variable	Percent
Gender	% of male decision-makers	37.12
Own_farmland	% of respondents who own farm land	95.04
Access_creditfacilities	% of respondents with access to credit facilities	57.68
Member_Farmersgroup	% of respondents who are members of farmers’ group	71.39
Farm Topography	% of respondents whose land has flat terrain	67.85
Exposure toclimate shocks	% of respondents exposed to climate shocks	90.31
Agrometinfor_trust	% of respondents who trust agrometeorological information services	37.83

^aDecision-maker is the household member responsible for making farming decisions.

^bkm = distance in kilometres

Table 3. Number of Climate-Smart Agricultural practices (CSA) applied in the past year

Dependent variable Statistics	Obs.	Mean	Std. Dev.	Min	Max
Number_CSAstrategies	423	2.03	1.70	0.00	13.00

Table 4. Standard poisson results on determinants of level of uptake of climate-smart agricultural practices

Variables for Number_CSAstrategies	Coef.	Std.Err.	P Value
Age	−0.010***	0.003	0.000
Education_level	−0.006	0.007	0.363
Gender	0.061	0.055	0.272
Hhold_size	0.002	0.012	0.889
Farm_Experience	0.007**	0.003	0.030
Prop_incomeallocatedfarming	0.236	0.230	0.305
Prop_underfarming	−0.251*	0.129	0.051
Own_farmland	−0.275**	0.124	0.027
Distance_MarketCrop	−0.016	0.019	0.386
Access_creditfacilities	−0.114**	0.055	0.038
Extensionvisits	0.025	0.016	0.114
Group Membership	0.006	0.086	0.943
Land Topography	−0.080	0.056	0.154
Number_agrometinfor	0.097**	0.038	0.011
Trainings	0.127***	0.030	0.000
Exposed_climateshocks	1.027***	0.141	0.000
Agrometinfor_trust	0.196***	0.060	0.001
Constant	−3.817	0.074	0.001
LR test for the model chi^2(17) = 2547.71 Prob > chi^2 = 0.0000***
Akaike crit. (AIC) = 1196.272 Bayesian crit. (BIC) = 1269.125

***, **, * = Significant at 1%, 5% and 10%, respectively

Table A1. NBR model results on determinants of level of uptake of Climate-Smart Agricultural practices

Variables for Number_CSAstrategies	Coef.	Std.Err.
Age_decisionmaker	−0.000	0.004
Education_level	0.010	0.010
Gender_decisionmaker	0.033	0.077
Hhold_size	0.020	0.015
Farm_Experience	0.003	0.004
Prop_incomeallocatedfarming	0.256	0.347
Prop_underfarmingAcres	0.054	0.192
Own_farmland	0.218	0.213
Distance_MarketCrop	0.023	0.022
Access_creditfacilities	−0.045	0.075
Frequency_extensionvisitsinayear	0.023	0.018
Member_Farmersgroup	0.001	0.090
Topography_yourfarm	−0.064	0.078
Number_agrometinfor	0.026	0.067
Trainingcount_12months	0.164***	0.042
Exposed_climateshocks	3.408***	0.710
Agrometinfor_trust	0.229***	0.075
Constant	−3.817	0.774
/lnalpha	−18.184	212.432
alpha	1.27e-08	2.69e-06
LR test of alpha = 0: chibar^2(01) = 1.8e-05 Prob ≥ chibar2 = 0.498
LR test for the model chi^2(17) = 338.970 Prob > chi2 = 0.000***
Akaike crit. (AIC) = 1198.272 Bayesian crit. (BIC) = 1275.172

*** = Significant at 1%