Adapting agriculture to climate change: institutional determinants of adoption of climate-smart agriculture among smallholder farmers in Kenya

Figures & data

Figure 1. Average annual rainfall and temperatures (1991–2020) for Kenya.

Source: Authors' computation of temperature and rainfall data from World Bank Climate Change Knowledge Portal (2021).

Figure 2. Number of undernourished people (2000–2019) in Kenya.

Source: Authors' computation of FAOSTAT data (2018).

Figure 3. Land use and trend in crop yields (2012–2017) in Kenya.

Source: Authors' computation of FAOSTAT data (2018).

Figure 4. Average projected monthly precipitation and temperature (2020–2039) for Kenya.

Source: Authors' computation of temperature and rainfall data from World Bank Climate Change Knowledge Portal (2021).

Figure 5. Stages in the technology adoption process.

Source: Rogers (2003).

Figure 6. Map showing the location of the study area.

Table 1. Proportion of sample size per ward.

Ward	Number of Potato Farmers	Proportion	Sample size
Mbaruk / Eburu	6,602	60.63	73
Morendat	4,287	39.37	47
Total	10,889	100	120

Source: Gilgil Sub-County (2019).

Table 2. Descriptive statistics for institutional factors among smallholder potato farmers.

Institutional factors	Frequency	Percentage
Access to credit
No	68	56.7
Yes	52	43.3
Sources of credit
SACCOs	36	69.2
Private money lenders	3	5.8
Relatives and friends	7	13.5
Banks	6	11.5
Amount of credit accessed annually
< KES 10,000	16	30.8
KES 10,001- KES 25,000	23	44.2
KES 25,001- KES 35,000	5	9.6
>KES 35,000	8	15.4
Trained on CSA
No	82	68.3
Yes	38	31.7
Time when first training on CSA was attended
Last season	22	57.9
One year ago	13	34.2
Two years ago	3	7.9
Three years ago and above
Number of trainings on CSA attended
One	13	34.2
Two	13	34.2
Three	6	15.8
Four and above	6	15.8
Source of training on CSA
Ministry of Agriculture	23	60.5
Private companies	6	15.8
NGOs	9	23.7
Universities
Access to NGO support on CSA
No	108	90
Yes	12	10
Kind of support received from NGOs on CSA
Extension services	7	58.3
Credit	4	33.3
Inputs	1	8.3
Farmers' group membership
No	67	55.8
Yes	53	44.2
Receive support from farmers' group on CSA
No	9	17
Yes	44	83
Kind of support received from farmers' group on CSA
Eases access to extension services	38	86.4
Eases access to credit	2	4.5
Eases access to inputs	4	9.1

Source: Own computation of survey data, 2021.

Table 3. Access to credit and adoption of climate-smart agriculture.

Climate-smart agricultural practices	Smallholder potato farmers (N = 120)
	No Access (n = 68)		With Access (n = 52)
	F	%	F	%
Rainwater harvesting and storage	54	79.4	46	88.5
Irrigation	23	33.8	15	28.8
Mulching	46	67.6	31	59.6
Minimal tillage	45	66.2	42	80.8
Improved crop varieties	36	52.9	35	67.3
Terracing	48	70.6	42	80.8
Drainage management	46	67.6	39	75.0
Intercropping	58	85.3	49	94.2
Agroforestry	55	80.9	47	90.4
Synthetic fertilizers	64	94.1	50	96.2
Composting	46	67.6	45	86.5
Furrow/ ridge planting	48	70.6	41	78.8
Crop rotation	52	76.5	48	92.3
Apical rooted cuttings (potato seedlings)	1	1.5	8	15.4
Mini-tubers	0	0.0	2	3.8

Source: Own computation of survey data, 2021.

Table 4. Training on and adoption of climate-smart agriculture.

Climate-smart agricultural practices	Smallholder potato farmers (N = 120)
	Not Trained (n = 82)		Trained (n = 38)
	F	%	F	%
Rainwater harvesting and storage	68	82.9	32	84.2
Irrigation	17	20.7	21	55.3
Mulching	47	57.3	30	78.9
Minimal tillage	57	69.5	30	78.9
Improved crop varieties	42	51.2	29	76.3
Terracing	60	73.2	30	78.9
Drainage management	55	67.1	30	78.9
Intercropping	74	90.2	33	86.8
Agroforestry	72	87.8	30	78.9
Synthetic fertilizers	79	96.3	35	92.1
Composting	61	74.4	30	78.9
Furrow/ ridge planting	63	76.8	26	68.4
Crop rotation	65	79.3	35	92.1
Apical rooted cuttings (potato seedlings)	7	8.5	2	5.3
Mini-tubers	1	1.2	1	2.6

Source: Own computation of survey data, 2021.

Table 5. Non-government organization support and adoption of climate-smart agriculture.

Climate-smart agricultural practices	Smallholder potato farmers (N = 120)
	Not Supported (n = 108)		Supported (n = 12)
	F	%	F	%
Rainwater harvesting and storage	89	82.4	11	91.7
Irrigation	31	28.7	7	58.3
Mulching	68	63.0	9	75.0
Minimal tillage	78	72.2	9	75.0
Improved crop varieties	64	59.3	7	58.3
Terracing	81	75.0	9	75.0
Drainage management	76	70.4	9	75.0
Intercropping	97	89.8	10	83.3
Agroforestry	94	87.0	8	66.7
Synthetic fertilizers	104	96.3	10	83.3
Composting	82	75.9	9	75.0
Furrow/ ridge planting	82	75.9	7	58.3
Crop rotation	89	82.4	11	91.7
Apical rooted cuttings (potato seedlings)	7	6.5	2	16.7
Mini-tubers	1	0.9	1	8.3

Source: Own computation of survey data, 2021.

Table 6. Farmer group membership and adoption of climate-smart agriculture.

Climate-smart agricultural practices	Smallholder potato farmers (120)
	No FGM (n = 67)		Has FGM (n = 53)
	F	%	F	%
Rainwater harvesting and storage	62	92.5	38	71.7
Irrigation	20	29.9	18	34.0
Mulching	39	58.2	38	71.7
Minimal tillage	46	68.7	41	77.4
Improved crop varieties	31	46.3	40	75.5
Terracing	50	74.6	40	75.5
Drainage management	45	67.2	40	75.5
Intercropping	58	86.6	49	92.5
Agroforestry	53	79.1	49	92.5
Synthetic fertilizers	62	92.5	52	98.1
Composting	45	67.2	46	86.8
Furrow/ ridge planting	47	70.1	42	79.2
Crop rotation	54	80.6	46	86.8
Apical rooted cuttings (potato seedlings)	3	4.50	6	11.3
Mini-tubers	1	1.50	1	1.9

Source: Own computation of survey data, 2021.

Figure 7. Extent of CSA adoption.

Table 7. Omnibus tests of model coefficients for institutional factors.

		Chi-square	df	Sig
Step 1	Step	10.219	4	0.037
	Block	10.219	4	0.037
	Model	10.219	4	0.037

Table 8. Institutional factors' model summary.

Step	−2 Log likelihood	Cox & Snell R Square	Nagelkerke R Square
1	124.741^a	0.082	0.121

^a Estimation terminated at iteration number 5 because parameter estimates changed by less than .001.

Table 9. Percentage accuracy classification table^a for institutional factors' model.

Observed			Predicted
			Adoption of CSA		Percentage Correct
			.00	1.00
Step 1	Adoption of CSA	.00	0	30	0.0
		1.00	0	90	100.0
	Overall Percentage				75.0

^a The cut value is .500.

Table 10. Hosmer and lemeshow test for institutional factors' BLRM.

Step	Chi-square	Df	Sig
1	10.458	6	.107

Table 11. Institutional variables in the binary logistic regression equation.

Institutional variables		B	S.E	Wald	df	Sig	Exp(B)
Step 1^a	Access to credit	1.072	0.504	4.524	1	0.033	2.922
	Trained on CSA	0.777	0.611	1.619	1	0.203	2.176
	Receive NGO support on CSA	−0.768	0.827	0.863	1	0.353	0.464
	Farmers' group membership	0.406	0.496	0.670	1	0.413	1.501
	Constant	0.423	0.301	1.970	1	0.160	1.526

^a Variable(s) entered on step 1: Access to credit, Trained on CSA, Receive NGO support on CSA, Farmers' group membership.

World Bank. (2021). World Bank Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/.

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FAOSTAT. (2018). FAOSTAT- data. http://www.fao.org/faostat/en/#data/GT.

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Rogers, E. M. (2003). Diffusion of innovations (5th Ed.). Free Press.

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Gilgil Sub-County. (2019). Circular references: Area seasonal crops. Unpublished.

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Data availability statement

The data that support the findings of this study are available from the corresponding author, AW, upon reasonable request.