Maize farmers’ adaptation to drought: Do risk attitudes and perceived risk probability matter?

Figures & data

Figure 1. Map of Central Region, Ghana showing the study area.

Table 1. Explaining independent variables in the regression model

Variable ($x_j)$	Definition	Description of how measured	A priori sign
$S{e}_j$	Sex	Dummy: male=1, female=0	-
$A{g}_j$	Age	Farmer’s age in years at last birthday	+,-
$E{d}_j$	Education	Number of years of formal education	+
$H{s}_j$	Household size	Number of people living in household	+
$F{e}_j$	Farm experience	Number of years of farming	+
$I{n}_j$	Income	Average income from farm	+
$C{r}_j$	Credit	Dummy: Inaccessible=1, 0=otherwise	-
$E{x}_j$	Extension	Dummy: access=1 (i.e. at least 4–7 extension contacts in a year), 0=otherwise	+
$AT{R}_j$	Attitudes Towards Risk (Risk C1…Risk 7)	Factor scores for estimated CPT parameter estimates ($\alpha ,\beta ,$λ,$\gamma ,\delta ,\text{isin},\eta$)	+,-
$PP{D}_j$	Farmers’ perceptions of the probability of drought	Computed probabilities from the farmers’ expectations of drought within the next 10 years	+

Table 2. Results of the principal component analysis

FACTOR LOADING
Risk parameter	Risk C1	Risk C2
$\alpha$	−0.74	−0.62
$\beta$	−0.61	0.77
$\gamma$	−0.97	0.19
$\delta$	0.85	−0.51
$\text{isin}$	−0.15	0.96
$\eta$	0.96	0.10
$\lambda$	−0.20	−0.97
VARIANCE EXPLAINED BY COMPONENTS
Component	Variance	Cumulative variance
1 (Risk C1)	0.57	0.57
2 (Risk C2)	0.39	0.96
3–7 (Risk C3–7)	0.04	1
COMPONENT INTERPRETATION
	Gain domain	Loss domain
	Risk Aversion increases ($RA\uparrow$)	Risk Seeking increases ($RS\uparrow$)
Risk C1	‘Inverted-S’ probability weighting increases ($IS\uparrow$)
	Decreasing weighting of gains (i.e. Pessimism increases - $PESS\uparrow$)	Decreasing weighting of losses (i.e. Optimism increases - $OPT\uparrow$)
Loss aversion decreases ($LA\downarrow$)
	Gain domain	Loss domain
	Risk Aversion increases ($RA\uparrow$)	Risk Aversion increases ($RA\uparrow$)
Risk C2	Increasing weighting of gains (i.e. Optimism increases - $OPT\uparrow$)	‘S’ probability weighting increases ($S\uparrow$)
Loss aversion decreases ($LA\downarrow$)

Table 3. Distribution of farmers’ subjective views of probabilities of occurrence of extreme drought and related weather events in the study area

Adverse climatic events	Subjective probability range					Mean probability (%)	Probability definition
	<5%	5–10%	11–25%	26–50%	>50%
Drought	4.7%	7.3%	6.5%	47.1%	34.3%	41.74 (19.70)	High
Unusually erratic rainfall	2.3%	3.9%	10.9%	57.3%	25.6%	40.06 (18.60)	High
Long period of intense heat	2.9%	2.1%	7.8%	54.2%	33.0%	43.44 (20.62)	High

Table 4. Perceived impacts of EWEACCs on the production/livelihoods of farmers

Adverse climatic conditions	Perceived impact scale					Mean	Overall perceived impact
	1	2	3	4	5
Drought	5.7%	4.2%	16.9%	27.6%	45.6%	4.03 (1.14)	High
Unusually erratic rainfall	7.0%	3.1%	38.0%	27.6%	24.2%	3.59 (1.10)	High
Long period of intense heat	3.9%	1.6%	17.2%	25.5%	51.8%	4.20 (1.03)	High

Interpreting key: 1.0–1.4 = very low impact, 1.5–2.4 = low impact, 2.5–3.4 = moderate impact, 3.5–4.4 = high impact 4.5–5.0 = very high impact

Prompt: impact implies the extent of damage to production

Table 5. Farmers’ knowledge, usage and perceptions of the riskiness of adaptation strategies

Adaptation strategies	Knowledge		Use before		Perceived riskiness		known as CCAS
	Freq	Percent (%)	Freq	Percent (%)	Safe (%)*	Risky (%)*	Freq	Percent (%)
Special new varieties	380	99.0	329	86.5	21.3	68.7	291	76.6
Irrigation and drainage systems designs	361	94.0	196	54.3	87.2	12.8	284	78.6
Mixed cropping	381	99.2	354	92.9	96.8	3.2	245	64.3
Mixed farming	351	91.4	187	53.3	87.1	12.9	173	49.3
Crop rotation	345	89.8	205	59.5	91.8	8.2	192	55.5
Shifting cultivation	348	90.6	182	52.3	87.9	12.1	195	56.0
Crop insurance	219	57.0	0	0.0	92.1	7.9	55	25.0
Adjustment in planting and/or harvest times	344	89.6	205	59.6	80.2	19.8	200	58.1
Soil conservation technologies	320	83.3	174	54.4	91.3	8.7	197	61.5
Integrated pest management	291	75.8	128	44.0	22.3	77.7	106	36.2

*Percentage response from farmers.

Table 6. Bayes probit results for the determinants of farmers’ decision to adopt new drought tolerant variety

Predicting variables	Probit Standard			Reversal Jump Probit
	$\theta$ (Mean)	Std	Pseudo p-value	Mean	Median	Prob. Inc
Int.	2.2*	0.25	<0.001	2.06	2.05	1
Risk C1	1.70*	0.99	0.086	1.41	1.46	0.74
Risk C2	0.96	1.04	0.35	0.28	0.00	0.32
Risk C3	1.21	1.08	0.26	0.47	0.00	0.41
Risk C4	−0.047	1.02	0.96	−0.009	0.00	0.24
Risk C5	0.12	1.13	0.91	−0.026	0.00	0.25
Risk C6	0.14	1.09	0.89	0.022	0.00	0.27
Risk C7	1.34	1.45	0.35	0.52	0.00	0.40
PPD	11.70*	1.53	<0.001	11.27	11.23	>0.99
Sex	−0.016	0.35	0.96	0.003	0.00	0.11
Age	0.133	1.03	0.89	−0.015	0.00	0.23
Education	0.971	0.75`	0.19	0.546	0.00	0.47
Family size	0.41	1.03	0.68	0.208	0.00	0.29
Farming experience	−0.82	0.80	0.30	−0.336	0.00	0.36
Income	1.98*	1.28	0.12	1.34	1.13	0.64
Accessibility of credit	0.95*	0.40	0.017	0.54	0.51	0.62
Agric. extension contact	2.02*	0.49	<0.001	1.72	1.70	>0.99

*significant at the 10% level; at least 90% of the posterior mass is either negative or positive for the affected variables’ parameter distributions (i.e. Confidence Level: 90%). Risk C1, C2-C7: factor scores for the PT parameters.

Figure 2. Farmers’ perceptions of climate change compared with trend in objective data.

Figure 3. Maize farmers’ decision to adopt drought tolerant variety or otherwise.

Figure 4. Framework of decision tree to analyse drought tolerant crop adoption problem (author’s construct).