The perception and determinants of agricultural technology adaptation of teff producers to climate change in North Shewa zone, Amhara Regional State, Ethiopia

Figures & data

Table

Author (year)	Location	Method	Result
Destaw and Fenta (2021)	Ambasel district, Northern Ethiopia	Multinomial logit (MNL) model.	The result from MNL analysis showed that age, family size, educational level, farm size, income, livestock holding, access to extension, distance to market, access to climate information, and agro-ecological zones were amongst the factors that had a significant influence on farmers’ choice of adaptation strategies. The basic barriers to climate change adaptation were lack of finance, shortage of land, inadequate climate information, lack of skill, and shortage of labour. The study recommends that strengthening interventions that enhance income-generating activities and access to climate information should be an integral part of climate change adaptation strategies.
Marie et al., 2020	Gondar Zuria district of north western Ethiopia.	Multinomial and binary logistic regression models	The result showed that age, gender, family size, farm income, and farm size had a significant influence on the farmers’ choice of climate change adaptation strategies. The result also revealed that crop failure, severe soil erosion, and shortages of water are the major climate change-related problems than others. The study recommends that future adaptation-related plans should focus on improving climate change information access, improving market access, and enhancing research on the use of rainwater harvesting technology.
Sedebo et al., 2021	Lemo district of southern Ethiopia	Stochastic frontier analysis	The model revealed that adaptation to climate change has a positive and significant contribution to the technical efficiency of major crops, wheat, and teff production. Other key determinants of technical efficiency are farming experience, education, access to extension services, livestock holdings, and farm household income. Overall, our study suggests a policy shift to promote smallholder farmers’ adaptation to climate change using climate-smart practices for an effective response to climate change impact while enhancing technical efficiency.
Sertse et al., 2021	Raya Azebo district of Ethiopia	Descriptive and interferential statistics	Results show that farm households perceived significant changes in the climate, such as an increase in temperature (overall), a decline in precipitation (overall), and altering patterns of rainfall. Furthermore, farmers’ reported risks include droughts, floods, and increased pests and disease attacks on the crops. This study recommends that improving rural households’ access to various institution-led services (i.e., climate information, advisory, and credit) improves their adaptation and livelihood resilience.
A. Asfaw et al., 2019	North central Ethiopia	Multinomial logit (MNL) model.	The results from the multinomial discrete choice model revealed that age and educational level of the head, family size, herd size, access to training, access to microfinance, extension services, remittance, and perceiving that climate change can be adapted influenced the selection of adaptations. This study recommends that overcoming financial constraint, strengthening extension service, providing timely information and early warning, intensifying irrigation, and land resource management would enable to enhance the adaptive capacity of smallholder farmers.
Tesfaye and Nayak (2022)	Gedeo zone, Ethiopia	Multivariate probit regression model	The study finds five major climate change adaptation options adopted by farm households, viz., agro-forestry, soil and water conservation, small-scale irrigation, crop diversification, and adjusting planting dates. Findings indicate that various demographics, socio-economic, institutional, and biophysical and climate change risk factors determine adoption of climate change adaptation strategies. The study suggests the significance of formulating different information opportunities such as local climate forums, access to media centres, and training centres for farmers can help to increase adaptation of farm households to climate change.
Mihiretu et al., 2020	Tekeze lowland, Ethiopia	Probit model	The result discovered that educational status, farm/nonfarm income, extension contact, access to climate information, and training were significant factors behind farmers’ response decision. The study recommended that future development interventions should provide prominent training and extension, and local meteorological service and agricultural inputs on top of creating non-agricultural income-generating opportunities would play a significant role in climate change adaptation.

Figure 1. Conceptual framework.

Source: Adopted from Mihiretu et al. (2020) and modified by the author.

Table 1. Sample size determination

No.	Name of kebele	Total population from each kebele	Total household of each kebele	Proportional sample$\mathit{P}i=\frac{n}{N}\times Xi$	Sampledhousehold
1	Jihur	6,521	1,461	378/7,019 × 1,461	79
2	Gerba	5,779	1,255	378/7,019 × 1,255	68
3	Woyramba	3,591	799	378/7,019 × 799	43
4	Amora Bet	5,247	1,298	378/7,019 × 1,298	70
5	Kombolcha	3,770	788	378/7,019 × 788	42
6	Bolo Giorgis	6,507	1,418	378/7,019 × 1,418	76

Table 2. Measurements, description of variables, expected sign and sources

Name of variable and symbol	Description and measurement of variables	Expected sign	Sources
Perception	Households were asked whether they perceive the undergoing climate change or not.	Dependent variable
Gender (gender)	Gender of the head of the household (1 = male, 0 = female	+	Tazeze et al., 2012; Nhemachena & Hassan, 2007
Education (education)	Year of education of the household (divided into four categories – 0 = illiterate, 1 = primary school, 2 = secondary school, 3 =diploma and above)	+	Deressa et al., 2009; Belay et al., 2017
Farm experience (farmexperiance)	Years of farm experience (continuous variable)	+	Maddison, 2007; Mairura et al., 2021
Livestock ownership (livestockownership)	Households’ number of livestock (continuous variable)	+	Mairura et al., 2021; Belay et al., 2017
Access to extension services (extension)	Access to extension services (it is binary: 1 = yes, 0 = no)	+	Nyang’au et al., 2021; Adeagbo et al., 2021
Cultivated land size (cultivatedlandsize)	It is a continuous variable measured in hectare	+	Liberio, 2012; Ojo & Baiyegunhi, 2021
Access to climatic information (Info.access)	Access of households to climate-related information (it is binary: 1 = yes, 0 = no)	+	Deressa et al., 2010; Maddison, 2007
Adoption (adopt)	It is a categorical variable (0 = non-adopter; 1 = changing planting date 2 = fertilizer; 3 = herbicide; 4 =irrigation; 5 = row planting; 6 = improved seed)	Dependent variable
Age (age)	Defined as the number of years from the date of birth to the day of the survey interview date in the full year. The continuous variable measured in year.	−	S. Asfaw et al., 2016; Milkias & Abdulahi, 2018
Marital status (mstr)	It is a categorical variable “0” if single, ”1” widowed, “2” divorced, and ”3” married	+	Giziew & Mebrate, 2019; Massresha et al., 2021
Family size (familysize)	The number of the family. It is a continuous variable.	+	Uddin et al., 2017; Zegeye & Read, 2021
Land tenure security (landtenur)	The right of individuals. It is a binary variable (1 = yes, 0 = no)	+	Paltasingh, 2018; Zeng et al., 2018
Availability of credit (credit)	It is a dummy variable, representing 1 if a household had credit access and 0 otherwise.	+	Teye & Quarshie, 2021; Vinholis et al., 2021
Income (income)	It is a continuous variable	+	Deressa et al., 2010; Massresha et al., 2021
Livestock ownership (livestockownership)	It is a continuous variable measured in number	+	Siyum et al., 2022; Tarekegn & Ayele, 2020
Market distance (distancefrommkt)	It is the distance from their living home to the market area. This is a continuous variable measured in kilometers.	−	Milkias & Abdulahi, 2018; Feyisa & Yildiz, 2020

Note: Explanatory variables being explained earlier as determinants of perception and also variables that determine the adoption.

Table 3. Agricultural technology adoption status across kebeles

Adoption	Kebele
	Amora Bet	Jihur	Gerba	Bolo Giorgis	Kombolcha	Woyramba	Total
Non-adopter (N)	22	31	12	28	19	17	129
(%)	31.43	39.24	17.65	36.84	45.24	39.53	34.13
Adopters Fertilizer (N)	7	13	15	4	0	0	39
(%)	10.00	16.46	22.06	5.26	0.00	0.00	10.32
Herbicide (N)	1	5	2	10	2	1	21
(%)	1.43	6.33	2.94	13.16	4.76	2.33	5.56
Irrigation (N)	7	8	12	2	2	5	36
(%)	10.00	10.13	17.65	2.63	4.76	11.63	9.52
Changing planting date (N)	1	4	7	10	1	6	29
(%)	1.43	5.06	10.29	13.16	2.38	13.95	7.67
Row planting (N)	0	2	16	5	0	12	35
(%)	0.00	2.53	23.53	6.58	0.00	27.91	9.26
Improved seed (N)	32	16	4	17	18	2	89
(%)	45.71	20.25	5.88	22.37	42.86	4.65	23.54
Total	70	79	68	76	42	43	378
	100.00	100.00	100.00	100.00	100.00	100.00	100.00

Source: Own survey, 2021/2022.

Table 4. Summary of age and family size of households

Variables	Kebele	N	Mean	St. Dev.	Min.	Max.	F-value (mvtest)
	Jihur	79	43.531	9.642	22	80	0.0071***^a–d
	Gerba	68	40.838	11.322	18	69
Age	Woyramba	43	46.441	12.717	20	67
	Amora Bet	70	39.028	10.799	20	75
	Kombolcha	42	43.357	11.356	20	65
	Bologiorgis	76	44	11.257	20	68
	Total	378	42.619	11.219	18	80
	Jihur	79	5.354	2.247	1	14	0.0351**^a–d
	Gerba	68	4.779	2.0793	1	9
Family size	Woyramba	43	5.651	2.399	1	11
	Amora Bet	70	4.485	1.700	1	10
	Kombolcha	42	4.809	2.132	1	9
	Bologiogis	76	4.894	1.949	1	10
	Total	378	4.970	2.091	1	14

Source: Survey data 2021/2022.

^aWilks’ lambda; ^bPillai’s trace; ^cLawley–Hotelling trace; ^dRoy’s largest root. N is number of observations.

Table 5. Summary of partial demographic distribution of households

Variables	Kebele														Chi2 & p-value
Gender	Amora Bet		Jihur		Gerba		Bolo Giorgis		Kombolcha		Woyramba		Total
	N	%	N	%	N	%	N	%	N	%	N	%	N	%
Female	9	12.8	10	12.6	2	2.94	6	7.89	7	16.67	2	4.65	36	9.52	9.1286 & 0.104
Male	61	87.1	69	87.3	66	97.0	70	92.11	35	83.33	41	95.35	342	90.48
Total	70	100	79	100	68	100	76	100	42	100	43	100	378	100
Marital status															30.2517 & 0.011
Married	60	85.7	73	92.4	63	92.6	66	86.84	34	80.95	40	93.02	336	88.8
Divorced	5	7.14	1	1.27	3	4.4	4	5.26	3	7.14	3	6.98	19	5.03
Widowed	5	7.14	1	1.27	2	2.94	6	7.89	5	11.90	0	0.0	19	5.03
Single	0	0.00	4	5.0	0	0.00	0	0.00	0	0.00	0	0.00	4	1.06
Total	70	100	79	100	68	100	76	100	42	100	43	100	378	100
Education															43.3265 & 0.000
Illiterate	12	17.1	35	44.3	26	38.2	44	57.89	10	23.81	11	25.58	138	36.51
Primary school	46	65.7	31	39.02	33	48.5	28	36.84	27	64.29	30	69.77	195	51.59
Secondary school	11	15.7	11	13.92	8	11.7	4	5.26	5	11.90	2	4.65	41	10.85
Diploma and above	1	1.43	2	2.53	1	1.47	0	0.00	0	0.00	0	0.00	4	1.06
Total	70	100	79	100	68	100	76	100	42	100	43	100	378	100

Source: Survey data 2021/2022.

Table 6. Livestock ownership and income

Variables	Kebele	N	Mean	St.Dev.	Min.	Max.	F-value (mvtest)
	Jihur	79	893.9082	542.5335	216.1	2876.5	7.99*** ^a–d
	Gerba	68	994.6949	485.2319	348.2	2929.7
Income	Woyramba	43	858.3314	355.7762	339.2	1710
	Amora Bet	70	580.1907	410.5395	111.2	1987.3
	Kombolcha	42	657.0417	456.5447	133.2	2029.5
	Bologiorgis	76	733.9916	364.7097	197.7	1608
	Total	378	791.4252	466.8471	111.2	2929.7
Livestock ownership	Jihur	79	5.986456	4.435093	0	26.85	18.11***^a–d
	Gerba	68	7.842647	4.123731	1.7	20.7
	Woyramba	43	8.58814	4.45897	1.74	20.7
	Amora Bet	70	3.423714	2.174693		9.85
	Kombolcha	42	4.710952	2.916684	0	11.6
	Bologiogis	76	4.894737	1.949899	1	10
	Total	378	4.970899	2.091209	1	14

Source: Survey data 2021/2022.

Notes: *** significant at 1%, ** significant at 5%, and * significant at 10%.

^aWilks’ lambda; ^bPillai’s trace; ^cLawley–Hotelling trace; ^dRoy’s largest root. N is number of observations.

Table 7. Farm experience and distribution of farm characteristics

Farm characteristics	Kebele												F-value
	Amora Bet		Jihur		Gerba		BoloGiorgis		Kombolcha		Woyramba
	N	Mean	N	Mean	N	Mean	N	Mean	N	Mean	N	Mean
Farm experience	70	21.47	79	23.07	68	22.51	76	26.39	42	26.64	43	26.88	2.233**
Cultivated land size (ha)	70	1.52	79	1.93	68	1.72	76	1.37	42	1.38	43	2.09	4.708***

Notes: *** significant at 1%, ** significant at 5%, and * significant at 10%.

Source: Survey 2021/2022.

Table 8. Summary of access to institutional setups

Institutional factors	Kebele														Chi2 & p-value
	Amora Bet		Jihur		Gerba		Bolo Giorgis		Kombolcha		Woyramb		Total
	N	%	N	%	N	%	N	%	N	%	N	%
Access to extension
No	5	7.14	4	5.06	8	11.76	5	6.58	5	11.90	2	4.65	29	7.67	4.13
Yes	65	92.86	75	94.94	60	88.24	71	93.42	37	88.10	41	95.35	349	92.33	&
Total	70	100	79	100	68	100	76	100	42	100	43	100	378	100	0.530
Access to information
No	16	22.86	4	5.06	9	13.24	9	11.84	7	16.67	3	6.98	48	12.70	12.605
Yes	54	77.14	75	94.94	59	86.76	67	88.16	35	83.33	40	93.02	330	83.30	&
Total	70	100	79	100	68	100	76	100	42	100	43	100	378	100	0.027
Access to credit
No	47	67.14	38	48.10	26	38.24	38	50.00	17	40.48	10	23.26	176	46.56	24.25
Yes	23	32.86	41	51.90	42	61.76	38	50.00	25	59.52	33	76.74	202	53.44	&
Total	70	100	79	100	68	100	76	100	42	100	43	100	378	100	0.000
Land tenure
No	17	24.29	6	7.59	4	5.88	6	7.89	9	21.43	2	4.65	44	11.64	21.32
Yes	53	75.71	73	92.41	64	94.12	70	92.11	33	78.57	41	95.35	334	88.36	&
Total	70	100	79	100	68	100	76	100	42	100	43	100	378	100	0.000

Source: Survey 2021/2022.

Table 9. Results of the Heckman probit model

	Estimated selection equation: perception				Estimated outcome equation: adoption
Variables	Coefficient		Std. Err	dy/dx	Coefficient	Std. Err	dy/dx
Gender	0.9501***		0.236	0.309	−0.972	0.76	−0.871
Age					0.008	0.02	0.007
Education	−0.046		0.122	−0.011	0.197	0.226	0.177
Marital status					−0.003	0.254	−0.003
Family size					−0.062	0.072	−0.055
Info. access	0.523**		0.231	0.148	−0.79	0.606	−0.708
Farm experience	0.021***		0.007	0.005	−0.02	0.019	−0.018
Land tenure					−0.326	0.435	−0.292
Farm size	−0.002		0.066	−0.001	−0.176	0.118	−0.158
Distance from market					0.048	0.014	0.043
Credit					0.107	0.265	0.096
Livestockownership	0.031*		0.019	0.007
Fertility	0.108		0.182	0.025
Extension	0.947***		0.262	0.314
Constant	−1.579***		0.434		7.451***	1.726
Mills lambda	2.718**		1.228
rho	1.000
sigma	2.718
Mean dependent var		0.810		SD dependent var		0.393
Number of obs Censored obs Uncensored obs		378.000 72 306		Wald chi2(11) Prob > chi2		29.251 0.0021

***p <0.001, **p <.05, *p<. The vacant spaces corresponding to each variable indicate that the variable is not included in either of the two equations.

Source: The Author’s Own Survey Result, 2021/2022.

Table 10. Results of the marginal effects of the multinomial logit model

Variables	Changing_ planting date		Fertilizer		Herbicide		Irregation		Row_planting		improved_seed
	Coeff(SE)	dy/dx	Coeff(SE)	dy/dx	Coeff(SE)	dy/dx	Coeff(SE)	dy/dx	Coeff(SE)	dy/dx	Coeff (SE)	dy/dx
Gender	-0.452 (0.863)	-0.015	1.203 (0.829)	0.013	2.024** (1.002)	0.046	1.805 (1.204)	0.098	1.288 (1.117)	-0.263	0.867* (0.489)	0.007
Education	0.662* (0.364)	0.070	0.0157 (0.274)	0.015	-0.731* (0.398)	-0.034	-0.430 (0.267)	0.044	-0.0886 (0.317)	0.025	0.130 (0.293)	-0.001
Marital status	11.95*** (0.637)	0.013	-0.554 (0.496)	-0.066	0.183 (0.403)	0.023	1.043** (0.414)	-0.909	13.65*** (0.856)	0.139	-0.399 (0.269)	-0.016
Age	-0.0200 (0.0306)	-0.002	-0.0139 (0.0318)	-0.004	0.000715 (0.0351)	-0.001	0.00420 (0.0313)	5.38e-07	-0.0136 (0.0309)	-0.021	0.0137 (0.0225)	-0.001
Family size	0.156 (0.141)	0.002	-0.182 (0.150)	-0.024	0.0229 (0.152)	0.005	-0.00844 (0.117)	0.085	0.156 (0.122)	0.040	-0.259*** (0.0983)	-0.027
Farm experience	0.0885** (0.0395)	0.010	0.00513 (0.0255)	0.002	0.0133 (0.0328)	0.008	-0.00683 (0.0252)	-0.003	0.0400 (0.0299)	0.019	-0.0132 (0.0188)	0.005
Land tenure	14.66*** (0.695)	0.055	16.41*** (0.401)	0 .141	-0.710 (0.835)	-0.056	0.917 (0.786)	0.061	-0.0439 (1.267)	-0.072	-0.121 (0.423)	-0.002
Credit	-0.216 (0.478)	-0.003	-0.343 (0.402)	-0.009	-0.649 (0.547)	-0.037	0.554 (0.410)	0.058	-0.193 (0.457)	-0.026	0.109 (0.305)	-0.029
Extension	1.833** (0.758)	0.041	-0.723 (0.798)	-0.015	-0.0330 (0.992)	0.008	-0.809 (0.710)	-0.092	-0.365 (0.846)	0.096	-0.180 (0.702)	-0.002
Income	0.0110 (0.0771)	1.80e-06	-0.00319 (0.0577)	3.14e-06	-0.00901 (0.0717)	-0.001	0.00428 (0.0448)	0.001	0.00796* (0.0474)	0.002	-0.0152*** (0.0448)	1.47e-06
Livestock ownership	0.113* (0.0640)	0.009	0.0344 (0.0576)	0.001	0.171** (0.0764)	0.073	-0.00539 (0.0450)	-0.003	0.258*** (0.0566)	-0.136	0.0593 (0.0539)	0.002
Fertility	-0.374 (0.519)	0.004	-0.771* (0.429)	-0.010	-0.981* (0.540)	-0.036	-0.146 (0.479)	0.021	-0.816 (0.499)	0 .161	-0.772** (0.342)	-0.006
Info. access	2.047** (0.908)	0.113	2.384** (1.097)	0.026	2.726** (1.183)	0.061	-0.283 (0.607)	-0.092	16.92*** (0.726)	-0.193	1.239** (0.491)	0.093
Distance from market	-0.0771** (0.0339)	-0.011	-0.00296 (0.0236)	-0.005	-0.0412 (0.0343)	-0.003	-0.0431* (0.0231)	-0.027	-0.0311 (0.0231)	-0.009	0.0570*** (0.0169)	-0.001
_cons	-6.416*** (1.986)		-17.28*** (1.811)		-4.779 (3.203)		-2.324 (1.800)		-7.686*** (2.263)		-0.100 (1.298)
Mean dependent var				4.741			SD dependent var			1.857
Pseudo r-squared				0.181			Number of obs			378.000
Chi-square				8163.345			Prob > chi2			0.000
Akaike crit. (AIC)				1260.153			Bayesian crit. (BIC)			1614.293

***p<.01, **p<.05, *p<.1. Source: The Author’s Own Survey Result, 2021/2022.

Table

Ho: variables are exogenous
Durbin (score) chi2(1)	= .042525 (p = 0.8366)
Wu-Hausman F(1,363)	= .040842 (p = 0.8400)

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