Impact of Agricultural Credit on Coffee Productivity: An Analysis of the Perceptions of Smallholder Farmers

Wanzala, Richard Wamalwa; Marwa, Nyankomo; Lwanga, Elizabeth Nanziri

doi:10.17306/j.jard.2024.01761

Figures & Tables

Graphical representation of the dependent variable (customized from Hueta et al., 2020, page 2)Source: own analysis.

Dendogram by Ward’s Linkage Clustering of the Regressand using Euclidean Distance MatrixSource: own analysis.

Variable loading of the generated principal componentSource: own analysis.

Scree plot of generated principal componentSource: own analysis.

The level of education of SHCFs (n = 174)Source: own primary data.

The Percentage of SHCFs as per Affiliation of Farmers’ Cooperative Societies (n=174)Source: own primary data.

Unrotated principal component matrix

Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	3.1557	0.8715	0.1578	0.1578
Comp2	2.2842	0.3963	0.1142	0.2720
Comp3	1.8879	0.1778	0.0944	0.3664
Comp4	1.7101	0.0985	0.0855	0.4519
Comp5	1.6116	0.2298	0.0806	0.5325
Comp6	1.3818	0.0811	0.0691	0.6016
Comp7	1.3007	0.2056	0.0650	0.6666
Comp8	1.0950	0.1394	0.0548	0.7214
Comp9	0.9556	0.0843	0.0478	0.7691
Comp10	0.8713	0.1085	0.0436	0.8127
Comp11	0.7629	0.1175	0.0381	0.8508
Comp12	0.6454	0.0746	0.0323	0.8831
Comp13	0.5707	0.0577	0.0285	0.9116
Comp14	0.5130	0.1616	0.0257	0.9373
Comp15	0.3515	0.0221	0.0176	0.9549
Comp16	0.3294	0.0770	0.0165	0.9713
Comp17	0.2523	0.1195	0.0126	0.9840
Comp18	0.1329	0.0284	0.0066	0.9906
Comp19	0.1045	0.0210	0.0052	0.9958
Comp20	0.0834	.	0.0042	1.0000

How farmers used their agricultural credit

Agricultural purpose	Frequency	Percentage (%)
Acquisition of new land	7	25.0
Land preparation (clearing, stumping among others)	10	35.7
Buying of inputs (fertilizers, agrochemicals, seedlings etc.)	25	89.2
Hiring of labour	23	82.1
Increase of acreage of cultivation	14	50.0

Sectoral loan topology: No_ of loan A/Cs, gross loans and NPLs-December 2018

	No of loan A/Cs	% of total	Gross loans KShs. M	% of total	Gross NPLs KShs. M	% of total
Personal/household	6,728,258	93.63	661,460	26.63	45,672	14.42
Trade	255,409	3.55	475,423	19.14	81,622	25.77
Real estate	28,050	0.39	376,237	15.15	47,033	14.85
Manufacturing	15,213	0.21	323,817	13.04	51,791	16.35
Transport and communication	30,455	0.42	164,271	6.61	14,674	4.63
Energy and water	2,186	0.03	109,613	4.41	6,859	2.17
Building and construction	10,559	0.15	102,837	4.14	23,692	7.48
Financial services	14,986	0.21	95,780	3.86	6,049	1.91
Agriculture	95,158	1.32	89,961	3.62	30,452	9.62
Tourism, restaurant and hotels	4,548	0.06	72,134	2.9	6,392	2.02
Mining and quarrying	1,143	0.02	11,987	0.48	2,478	0.78

Total	7,185,965	100	2,483,518	100	316,712	100

Perceptions of all farmers (borrowers and non-borrowers)

Regressor	Coefficient	Odds ratio	Standard error	p-value
Acquisition of agrochemicals and fertilizers	2.040	7.691	0.811	0.012
Increased use of hired labour	−1.111	0.329	0.580	0.056
Increased use of optimal combination of inputs	−2.045	0.129	0.735	0.005
Annual profit per acre	3.262	26.114	0.612	0.000
Constant	1.447	4.251	0.717	0.044

Percentages of SHCFs perceptions on study variables

Theme	Variable	SHFs borrowers (n = 87)		SHCFs non-borrowers Credit (n = 87)		Overall perception (n = 174)

		perceptions		perceptions		perceptions

		yes	no	yes	no	yes	no

1	2	3	4	5	6	7	8
Demand for agricultural inputs	FDI1	10	90	5	95	7	93
	FDI2	6	94	2	98	4	96
	FDI3	34	66	40	60	37	63
	FDI4	87	13	79	21	83	17
	FDI5	29	71	38	62	33	67
	FDI6	31	69	48	52	40	60

Demand for labour	FDL1	28	72	23	77	25	75
	FDL2	53	47	44	56	48	52
	FDL3	64	36	84	16	74	26
	FDL4	23	77	22	78	22	78
	FDL5	7	93	1	99	4	96

Improved efficiency	FIE1	87	13	82	18	84	16
	FIE2	41	59	56	44	49	51
	FIE3	37	63	52	48	44	56
	FIE4	13	87	8	92	10	90
	FIE5	55	45	17	83	36	64

Returns	FRT1	77	23	78	22	78	22
	FRT2	25	75	32	68	29	71
	FRT3	59	41	49	51	54	46
	FRT4	32	68	29	71	30	70

Risk	RISKL	48	52	56	44	52	48
Risk	RISKD	62	38	90	10	64	36

The perceptions of farmers in FGDs as per the variables of the study

Theme	Variable	Symbol	Number of FDG (borrowers)	Number of FDG (non-borrowers)

1	2	3	4	5
Demand for inputs	Payment of leasing land	FDI1	6	3
	Buying of land	FDI2	5	3
	Accessing both printed and electronic information	FDI3	7	6
	Acquisition of agrochemicals and fertilizers	FDI4	11	11
	Acquisition of tree seedlings	FDI5	5	8
	Acquisition of manure	FDI6	3	10
Demand for labor	Increased use of child labor on the coffee farm	FDL1	5	8
	Increased use of labor from other members of your family apart from children on the farm	FDL2	5	10
	Increased use of hired labour	FDL3	11	11
	Increased use of ox-plough	FDL4	8	4
	Increased use of tractor	FDL5	6	3
Efficiency of production	Increased use of optimal combination of inputs	FIE1	11	11
	Increase in area of farming of coffee	FIE2	8	7
	Replacement of old trees with improved varieties	FIE3	8	4
	Increased access to extension services	FIE4	9	6
	Increase of the cost of labour	FIE5	10	9
Returns/profits	Annual profit per acre	FRT1	11	11
	Increase in numbers of shares for farmers in SACCO	FRT2	9	6
	Increase in farmers’ wealth	FRT3	10	8
	Investing in other business	FRT4	7	5
Risk	Risk of making loss	RISKL	3	9
Risk	Risk of loan default	RISKD	7	10

Summary statistics of regressors and regressand

Variable	Mean	Standard deviation	Skewness	Kurtosis
Acquisition of agrochemicals and fertilizers	0.827	0.378	−1.734	4.008
Increased use of hired labour	0.741	0.439	−1.102	2.215
Increased use of optimal combination of inputs	0.844	0.363	−1.904	4.628
Annual profit per acre	0.781	0.414	−1.363	2.858
Influence of agricultural credit on coffee productivity	0.787	0.410	−1.404	2.972

The perception of key informants as per the variables of the study

Theme	Variable	Symbol	Number of KIs	Frequency
Demand for inputs	Acquisition of agrochemicals and fertilizers	FDI4	15	42
Demand for inputs	Acquisition of tree seedlings	FDI5	9	–
Demand for labor	Increased use of child labor on the coffee farm	FDL1	4	–
Demand for labor	Increased use of hired labour	FDL3	13	36
Efficiency of production	Increased use of optimal combination of inputs	FIE1	15	40
Efficiency of production	Increase in area of farming of coffee	FIE2	7
Returns/profits	Annual profit per acre	FRT1	13	48
Returns/profits	Increase in farmers’ wealth	FRT3	10	–
	Investing in other business	FRT4	8	–
Risk	Risk of loan default	RISKD	11	27

Sample size for the study

S/No.	FCS	Number of Borrowers	Number of Non-borrowers
1.	Gathage FCS	7	7
2.	Gititu FCS	4	4
3.	Ichaweri FCS	4	4
4.	Komothai FCS	5	5
5.	Muhara FCS	6	6
6.	Ndumberi FCS	7	7
7.	New Gatukuyu FCS	14	14
8.	Nyakiri FCS	4	4
9.	Ritho FCS	14	14
10.	Theta FCS	5	5
11.	Thirirka FCS	17	17

Total		87	87

Perceptions of non-borrowers

Regressor	Coefficient	Odds ratio	Standard error	p-value
Acquisition of agrochemicals and fertilizers	3.116	22.558	0.877	0.000
Increased use of hired labour	−0.343	0.709	0.807	0.671
Increased use of optimal combination of inputs	−1.706	0.181	0.998	0.088
Annual profit per acre	−0.732	0.480	0.806	0.364
Constant	1.302	3.677	1.078	0.227

The cluster average scores (four clusters) n = 87 (borrowers)

Variables	Cluster

	1	2	3	4
FDI4	1.00000	1.00000	0.11111	1.00000
FDI5	0.00000	1.00000	0.44444	0.09091
FDI6	0.29412	0.52941	0.22222	0.25000
FDL1	0.35294	0.05882	0.22222	0.34091
FDL2	0.58824	0.70588	0.66667	0.40909
FDL3	0.88235	0.88235	0.00000	0.95455
FDL4	0.29412	0.11765	0.22222	0.25000
FDL5	0.17647	0.00000	0.00000	0.06818
FIE1	0.82353	0.76471	0.33333	1.00000
FIE2	0.11765	0.23529	0.77778	0.52273
FIE3	0.41176	0.52941	0.11111	0.34091
FIE4	0.17647	0.11765	0.11111	0.11364
FIE5	0.23529	0.41176	0.33333	0.77273
FRT1	1.00000	1.00000	0.33333	1.00000
FRT2	0.23529	0.41176	0.44444	0.15909
FRT3	0.05882	0.52941	0.77778	0.77273
FRT4	0.94118	0.00000	0.22222	0.22727

Eigenvectors for principal components

Variable	Comp1	Comp2	Comp3	Comp4	Comp5	Comp6	Comp7	Comp8	Unexplained

1	2	3	4	5	6	7	8	9	10
FDI1	0.1115	−0.0533	0.2176	0.5258	0.1113	−0.1245	−0.1146	−0.0830	0.3261
FDI2	−0.1065	0.4160	0.1683	0.0590	−0.2442	−0.0291	0.3567	0.1344	0.2270
FDI3	0.0529	−0.0357	0.2362	0.1486	0.3471	0.3461	−0.0219	−0.1446	0.4619
FDI4	0.4944	0.1238	0.1275	−0.0796	−0.0902	−0.0390	0.0355	−0.0104	0.1352
FDI5	−0.1754	0.4825	0.2197	−0.0218	−0.1146	−0.0646	0.1392	0.1408	0.2053
FDI6	−0.0561	0.1487	0.3917	0.1716	0.0591	0.1963	−0.2046	−0.1071	0.4737
FDL1	0.1022	−0.1254	−0.3200	0.1789	0.2709	0.0899	0.2979	0.1219	0.4220
FDL2	−0.0378	0.1209	−0.0923	0.3717	0.1264	−0.4784	0.1781	0.1698	0.2950
FDL3	0.4363	0.0877	0.1089	−0.0427	0.1299	0.0315	0.1059	0.0977	0.3025
FDL4	0.0545	−0.1425	−0.1907	0.1596	−0.1286	−0.4570	−0.0343	0.0773	0.5087
FDL5	0.1024	−0.1024	−0.1562	0.1538	0.0841	0.1361	0.3628	−0.4202	0.4549
FIE1	0.4239	0.0809	0.0317	−0.0424	0.1117	−0.0505	−0.0855	0.0881	0.3713
FIE2	−0.1724	−0.1088	0.3261	−0.4406	0.3278	−0.1250	0.2149	0.0523	0.0887
FIE3	0.1225	0.1733	−0.3451	0.0214	−0.4618	0.3715	−0.1569	−0.0010	0.0921
FIE4	0.0406	−0.0718	−0.0980	0.0474	0.0691	0.3618	0.4868	0.4404	0.2519
FIE5	0.0889	−0.0310	−0.1681	−0.2717	0.2354	−0.0393	−0.3485	0.4724	0.2994
FRT1	0.4819	0.1443	0.1327	−0.0771	−0.0407	−0.1207	0.0313	−0.0579	0.1484
FRT2	−0.0809	0.0672	0.0818	0.3964	0.1277	0.2259	−0.2757	0.4157	0.3029
FRT3	−0.0508	0.3582	−0.3325	−0.0361	0.4048	−0.0079	−0.1338	−0.2802	0.1144
FRT4	0.0633	−0.5250	0.2606	0.0618	−0.2787	0.0081	0.0269	0.0690	0.0916

Perceptions of borrowers

Regressor	Coefficient	Odds ratio	Standard error	p-value
Acquisition of agrochemicals and fertilizers	2.470	11.821	1.063	0.020
Increased use of hired labour	−2.146	0.116	1.035	0.038
Increased use of optimal combination of inputs	−4.278	0.013	1.515	0.005
Annual profit per acre	4.128	62.075	1.100	0.000
Constant	1.729	5.636	1.119	0.122

Impact of Agricultural Credit on Coffee Productivity: An Analysis of the Perceptions of Smallholder Farmers

Figures & Tables

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Fig. 6.

Unrotated principal component matrix

How farmers used their agricultural credit

Sectoral loan topology: No_ of loan A/Cs, gross loans and NPLs-December 2018

Perceptions of all farmers (borrowers and non-borrowers)

Percentages of SHCFs perceptions on study variables

The perceptions of farmers in FGDs as per the variables of the study

Summary statistics of regressors and regressand

The perception of key informants as per the variables of the study

Sample size for the study

Perceptions of non-borrowers

The cluster average scores (four clusters) n = 87 (borrowers)

Eigenvectors for principal components

Perceptions of borrowers

Paradigm

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