Psycho-social factors influencing the adoption of sustainable intensification practices by smallholder rice farmers in Northern Ghana

Figures & data

Table 1. Constructs, definitions and their measurement.

Construct	Indicator	Definition	Perceived effect
Performance Expectancy (PE)	PE1	the extent to which a farmer believes that SIPs will help them improve climate change resilience	SIPs are generally helpful in lowland rice production
	PE2		SIPs enable us to mitigate the effects of climate variability
	PE3		Employing SIPs will increase my yield
	PE4		SIPs will increase farm income
	PE5		SIPs will improve soil fertility
Effort Expectancy (EE)	EE1	the ease associated with the use of SIPs	SIPs are easy to use
	EE2		Learning to use SIPs is easy
Social Influence (SI)	SF1	the extent to which a farmer perceives that people in their community believe they should adopt SIP	People who advise me think that I should adopt SIPs
	SF2		People who are important to me think that I should adopt SIPs
	SF3		Extension officers are supportive of the adoption and use of SIPs
	SF4		Other smallholder farmers are using SIPs
	SF5		Commercial farmers in my community are using SIPs
	SF6		Most farmers in my community expect me to use SIPs
Facilitating Conditions (FC)	FC1	the measure of a farmer’s belief that organisational and technical infrastructure exists to support the implementation of SIPs	The government supports the implementation of SIPs
	FC2		I have the resources necessary to implement SIPs
	FC3		I have the knowledge necessary to practice SIPs
	FC4		I have access to support when I encounter problems while implementing SIPs
Attitude (ATT)	ATT1	A farmer’s positive or negative feelings about implementing SIPs’	Practising SI is a good idea
	ATT2		I will like to practice SI
	ATT3		I believe that practising SI will be interesting
	ATT4		I think SIPs will increase my yield
Behavioural Intention (BI)	BI1	a measure of the strength of a farmer’s intention to adopt SIPs	I intend to use additional SIPs next farming season
	BI2		I predict I will use additional SIPs next farming season
	BI3		I have plans to use additional SIPs in the near future

Source: Adapted from Thomas et al. (2013); Dwivedi et al. (2019).

Figure 1. Modified UTAUT model.

Source: Dwivedi et al. (2019).

Figure 2. The SEM model.

Source: Stein et al. (2012).

Figure 3. Structural model.

Table 2. Farmer perception of SIP adoption.

Construct	Perceived effect	Mode
Performance Expectancy (PE)	SIPs are generally helpful in lowland rice production	Agree
	SIPs enable us to mitigate the effects of climate variability	Strongly agree
	Employing SIPs will increase my yield	Strongly agree
	SIPs will increase farm income	Agree
	SIPs will improve soil fertility	Agree
Effort Expectancy (EE)	SIPs are easy to use	Undecided
	Learning to use SIPs is easy	Agree
Social Influence (SI)	People who advise me think that I should adopt SIPs	Agree
	People who are important to me think that I should adopt SIPs	Agree
	Extension officers are supportive of the adoption and use of SIPs	Agree
	Other smallholder farmers are using SIPs	Agree
	Commercial farmers in my community are using SIPs	Agree
	Most farmers in my community expect me to use SIPs	Agree
Facilitating Conditions (FC)	The government supports the implementation of SIPs	Strongly agree
	I have the resources necessary to implement SIPs	Undecided
	I have the knowledge necessary to practice SIPs	Agree
	I have access to support when I encounter problems while implementing SIPs	Strongly disagree
Attitude (ATT)	Practising SI is a good idea	Strongly agree
	I will like to practice SI	Strongly agree
	I believe that practising SI will be interesting	Agree
	I think SIPs will increase my yield	Strongly agree
Behavioural Intention (BI)	I intend to use additional SIPs next farming season	Undecided
	I predict I will use additional SIPs next farming season	Undecided
	I have plans to use additional SIPs in the near future	Agree

Source: Field data, 2020.

Table 3. Available SIPs, farmers’ understanding of the role in mitigating effects of climate variability and associated constraints.

Variable	Adoption rate (%)	Role in mitigating effects of climate variability	Constraints to adoption
Mechanised ploughing	97.5	Allows broadcasting of seed; Faster land preparation	Non-timely access
Fertiliser	94.6	Ensures higher yields; Allows faster crop growth	Non-timely access
Weedicide	93.4	Mitigates labour shortage; Reduces labour cost	Non-timely access
Early Planting	41.9	Mitigate effects of uncertain rains onset; Reduced flood risk	None identified
Pesticide	32.0	Effective in controlling pests	None identified
Bunding	31.1	Water retention; Controls flooding	Labour intensive
Improved Varieties	29.5	Mitigate effects of flooding; Mitigate effects of diseases; Mitigate the impact of drought; Higher yields	Poor access
Mechanised harvesting	27.4	Mitigate effects of flooding; Mitigate the effects of bushfires; Prevent the loss of harvest to livestock	Poor access; Unaffordable
Dibbling	20.7	Higher yields; Easier weed control	Labour intensive; Time-consuming
Row Planting	14.9	Higher yields; Easier weed control	Labour intensive; Time-consuming
Conservation Tillage	14.9	Labour saving; Cost-saving	Difficult broadcasting seed
Improved Seed	11.2	Higher yields	Unaffordable
Farm residue retention	4.6	Higher yields; Soil water retention	Bushfires; Livestock
Late Planting	3.3	Reduced flood risk	None identified
Crop Rotation	2.1	Improved soil fertility	Inadequate land
IPM	0.4	Improved soil fertility; Reduces disease incidence	Labour intensive; Poor knowledge
Intercropping	0	Improved soil fertility	Frequent floods

Source: Field survey, 2020.

Table 4. Model fit statistics.

Fit Statistic	Value	Description
RMSEA	0.028	Root mean squared error of approximation
SRMR	0.054	Standardised root mean square residual

Table 5. Test of reliability.

Item	Item-test Correlation	Ave. Inter-Item Covariance	Alpha
PE1	0.7673	0.271943	0.9405
PE2	0.8266	0.2667022	0.9395
PE3	0.8228	0.2626046	0.9393
PE4	0.7687	0.2671955	0.9402
PE5	0.7913	0.2661003	0.9399
EE1	0.7425	0.2673104	0.9406
EE2	0.7208	0.2689358	0.9409
SF1	0.7499	0.2663759	0.9405
SF2	0.6832	0.2697952	0.9415
SF3	0.7438	0.2682521	0.9406
SF4	0.7651	0.2681269	0.9403
SF5	0.6721	0.2718409	0.9416
SF6	0.751	0.2674061	0.9405
FC1	0.7739	0.2651142	0.9401
FC2	0.7162	0.2670575	0.941
FC3	0.654	0.2691078	0.9421
FC4	0.0168	0.2981066	0.9482
ATT1	0.8589	0.2514588	0.9387
ATT2	0.2156	0.2936752	0.9456
ATT3	0.7617	0.272092	0.9406
ATT4	−0.0643	0.2978491	0.9462
BI1	0.8264	0.2586102	0.9392
BI2	0.3408	0.285244	0.9464
BI3	0.0616	0.2965791	0.9464
Test scale		0.2723944	0.9441

Table 6. Output from SEM (direct effects).

Standardised	Coefficient	Std. Err	z	P>|z|	[95% conf. interval]
Structural Model
No. of SIPs Adopted
BI	0.726	0.070	10.410	0.000	0.589	0.862
FC	0.258	0.126	2.040	0.041	0.010	0.506
_cons	2.434	0.147	16.560	0.000	2.146	2.722
BI
PE	−0.660	0.099	−6.680	0.000	−0.854	−0.467
EE	−0.271	0.148	−1.830	0.067	−0.561	0.018
SI	0.074	0.197	0.380	0.007	−0.312	0.460
FC	0.442	0.177	−2.500	0.013	−0.790	−0.095
ATT	−0.839	0.035	−24.050	0.000	−0.908	−0.771
Measurement Model
PE1
PE	0.802	0.026	30.900	0.000	0.751	0.853
_cons	2.547	0.133	19.190	0.000	2.287	2.807
PE2
PE	0.824	0.024	34.730	0.000	0.778	0.871
_cons	2.367	0.126	18.850	0.000	2.121	2.613
PE3
PE	0.883	0.017	50.680	0.000	0.849	0.917
_cons	2.184	0.119	18.420	0.000	1.952	2.416
PE4
PE	0.865	0.019	44.960	0.000	0.828	0.903
_cons	2.349	0.125	18.810	0.000	2.104	2.593
PE5
PE	0.857	0.020	42.350	0.000	0.817	0.897
_cons	2.383	0.126	18.880	0.000	2.136	2.631
EE1
EE	0.854	0.027	31.310	0.000	0.800	0.907
_cons	2.741	0.129	21.300	0.000	2.489	2.993
EE2
EE	0.845	0.027	30.970	0.000	0.791	0.898
_cons	2.869	0.133	21.570	0.000	2.608	3.129
SI1
SF	0.784	0.028	28.310	0.000	0.730	0.839
_cons	2.731	0.132	20.770	0.000	2.474	2.989
SI2
SI	0.752	0.031	24.510	0.000	0.692	0.813
_cons	2.742	0.132	20.730	0.000	2.483	3.001
SI3
SF	0.690	0.035	19.660	0.000	0.621	0.759
_cons	2.633	0.129	20.340	0.000	2.379	2.886
SI4
SF	0.757	0.030	25.200	0.000	0.698	0.816
_cons	3.129	0.147	21.330	0.000	2.841	3.416
SI5
SF	0.588	0.043	13.640	0.000	0.504	0.673
_cons	2.804	0.137	20.420	0.000	2.535	3.073
SI6
SF	0.772	0.029	26.790	0.000	0.715	0.828
_cons	2.951	0.140	21.140	0.000	2.677	3.224
FC1
FC	0.678	0.051	13.290	0.000	0.578	0.778
_cons	2.242	0.121	18.570	0.000	2.005	2.479
FC2
FC	0.825	0.038	21.670	0.000	0.750	0.899
_cons	3.040	0.153	19.880	0.000	2.741	3.340
FC3
FC	0.753	0.040	18.650	0.000	0.674	0.832
_cons	2.964	0.150	19.810	0.000	2.671	3.257

Table 7. Path analysis for hypothesis testing.

Hypothesis	Stand. estimates	S.E	z-value	Decision
H1: PE BI	−0.660	0.099	−6.680***	Supported
H2: EE BI	−0.271	0.148	−1.830	Not supported
H3: SI BI	0.074	0.197	0.380***	Supported
H4:FC BI	0.442	0.177	2.500**	Supported
H5: ATT BI	−0.839	0.035	−24.050***	Supported
H6: BI No_SIPs	0.726	0.070	10.410***	Supported
H7: FC No_SIPs	0.258	0.126	2.040**	Supported

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