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Analyzing the Determinants and Extent of Crop Diversification among Smallholder Coffee Farmers in Kirinyaga Central and East Sub-Counties, Kirinyaga County, Kenya Cover

Analyzing the Determinants and Extent of Crop Diversification among Smallholder Coffee Farmers in Kirinyaga Central and East Sub-Counties, Kirinyaga County, Kenya

Open Access
|Jun 2024

Figures & Tables

Fig. 1.

Thematic map of Kirinyaga Central Sub-County and Kirinyaga East Sub-CountySource: GeoCurrents (2015).
Thematic map of Kirinyaga Central Sub-County and Kirinyaga East Sub-CountySource: GeoCurrents (2015).

Fig. 2.

Crop diversification distribution in the study areaSource: field survey, 2022.
Crop diversification distribution in the study areaSource: field survey, 2022.

Fig. 3.

Kernel density estimate for crop diversification indexSource: field survey, 2022.
Kernel density estimate for crop diversification indexSource: field survey, 2022.

Demographic characteristics of categorical variables

VariablesCategoryFrequencyPercent.
Household headFemale12531.17
Male27668.83
Diversification (years)Less than 10 years15037.4
10–20 years11528.68
Above 20 years13633.92
Extension (access)No11227.93
Yes28972.07
Extension oftenOnce175.88
Twice5318.34
Thrice8529.41
More than thrice13446
Group membershipNo204.99
Yes38195.01
Group benefitsAgricultural information18047.24
Marketing information3910.24
Credit access9625.2
Input access6617.32
Credit accessNo5112.72
Yes35087.28
DiversifiersNo215.24
Yes38094.76

Crop diversification level of sampled households

Crop Diversification IndexFrequencyPercent.
0–0.1204.99
0.10–0.29824.44
0.30–0.416340.65
0.50–0.610826.93
0.70–0.8122.99

Sample size of smallholder coffee farmers in the study area

Sub-CountyAEZsLocationSub-LocationPopulationSample Size
Kirinyaga CentralUM1MutiraKabari91120
InoiMbeti50011
UM2KerugoyaKaitheri1 90042
KoromaNduini92220
UM3KanyekiniKianjege2 17448
KutusKangu37008
Kirinyaga EastUM1NgariamaRungeto60013
KarumandiKiaruri2 22250
UM2BaragwiRwambiti3 20071
NjukiiniNgiriambu2 80062
UM3KirimaMutige1 49033
KabareRukenya1 33130

Totals 18 420408

Truncated regression estimates for determinants of extent of crop diversification

VariablesCoeff.Std. Errzp-value
Gender of household head (Yes=1, No=0)−0.053**0.018−2.9600.003
Age of household head (Years)0.0110.011.1000.241
Access to extension services (Yes=1, No=0)0.052*0.0202.5400.011
Distance to market (km)0.0020.0050.5400.633
Credit access (Yes=1, No)0.0440.0271.6300.102
Land size (ha)0.067**0.0232.9100.004
Education level (years)0.005*0.0021.9800.048
Household size (number)0.013*0.0052.3700.018
Constant0.106***0.0273.9260.000
LR chi2(8)80.46            Pseudo0.618
Prob > chi2 ( )0.000            Log likelihood105.327
No. Observation401

Demographic characteristics of discrete variables

VariablesMeanStd. Dev
Age (years)48.0111.43
Education level (years)10.823.19
Household size (No.)3.631.05
Distance (km)2.360.09
Land size (ha)1.590.41
Cropped land (ha)0.580.13

Summary of the explanatory variables used in the double-hurdle model

VariableDescriptionExp sign
Explained variables
Crop diversification choiceBinary: 1 if the farmer uses at least 1 crop diversification strategy, 0 if otherwise
Crop diversification extentCrop Diversification Index (0–1)
Explanatory variables
Gender of the household headBinary: 1 if the gender of the farmer is male, 0 if otherwise+/−
Age of the household headContinuous: Age of household head (Years)+/−
Education level of the household headContinuous: Highest level of education attained+
Extension accessBinary: 1 if the farmer had accessed extension services, 0 if otherwise+
Group membershipBinary: 1 if the farmer was a member of a community organization, 0 if otherwise+
Household sizeContinuous: Number of dependents in the household+
Distance to marketContinuous: Walking distance to market in km+/−
Access to creditBinary: 1 if the farmer had received inputs credit, 0 if otherwise+
Total cultivated land sizeContinuous: The total cultivated land size (Ha)+/−

Probit regression estimates for determinants of crop diversifications

VariablesCoeff.Std. Errzp-valuedy/dx
Gender of household head−0.4660.306−1.5200.129−0.062
Age of household head (years)−0.0060.011−0.5500.5810.001
Household size (No.)0.143*0.0871.9800.0990.016
Access to Extension services0.704**0.2612.7000.0070.065
Cooperative membership0.5630.4871.1600.2470.013
Distance to the market (km)−0.1020.069−1.4700.141−0.003
Credit access−0.6180.550−1.1200.2620.027
Land size (ha)1.058**0.3223.2900.0010.088
Education level (years)−0.0080.041−0.2000.8380.0047
_cons−0.227***0.00828.3750.000

lnsigma
_cons−1.8330.040−46.1300.000
/sigma0.1600.006
LR chi2(8)80.46Pseudo0.618
Prob > chi2 ( )0.000Log likelihood105.327
No. Observation401
DOI: https://doi.org/10.17306/j.jard.2024.01744 | Journal eISSN: 1899-5772 | Journal ISSN: 1899-5241
Language: English
Page range: 201 - 217
Accepted on: May 29, 2024
Published on: Jun 30, 2024
Published by: The University of Life Sciences in Poznań
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Pauline Kananu Micheni, Geofrey Kingori Gathungu, Dennis K. Muriithi, published by The University of Life Sciences in Poznań
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.