Determinants of product upgrading in Seka Chekorsa District, Jimma Zone, Oromia Region, Ethiopia

Figures & data

Figure 1. Conceptual framework.

Figure 2. Map of the study area.

Source: Adopted and manipulated from Ethiopian map.

Table 1. Proportional distribution of sample households in each kebele.

No	Kebeles	Total number of tomato producers in each kebele	Number of sampled households (proportional to sample size) in each kebele
1	Dabo yaya	248	57
2	Buyo Kechema	110	25
3	Ushane koche	191	44
4	Boba roge	140	32
	Total	689	158

Source: Own computation from survey result, 2022.

Table 2. Summary of hypothesis and variables relationship.

Explanatory variables	Type of variable	Measurement	Dependent variable	References
			Product upgrading
Sex of the household head	Dummy	1 = male 0 = female	+(M)	Alwang et al., 2019; Nnanna Mba et al., 2015
Age of household head	Continuous	Year	−	Adeyonu et al., 2017
Family size	Continuous	Adult-equivalent	+	Alwang et al., 2019
Year of school of household head	Continuous	Formal grades	+	Doti, 2018
TLU	Continuous	Number of TLU	+	Bati & Aman, 2017
Off-farm activity	Dummy	1 = participated 0 = otherwise	−	Kinuthia, 2019
Access to credit	Dummy	1 = has access to credit 0 = otherwise	+	Alwang et al., 2019
Access to market information	Dummy	1 = has market information 0 = otherwise	+	Molla et al., 2022
Access to extension service	Dummy	1 = has access 0 = otherwise	+	Danso-Abbeam et al., 2017
Access to training	Dummy	1 = has access 0 = otherwise	+	Alwang et al., 2019
Tomato farming experience	Continuous	Number of years	+	Tegegne, 2017
Distance to the nearest market	Continuous	Walking hour	−	Alwang et al., 2019
Access to improved seed	Dummy	1 = has access 0 = otherwise	+	Adunea & Fekadu, 2019
Access to pesticide	Dummy	1 = has access 0 = otherwise	+	Khan & Damalas, 2015

Table 3. Household’s participation in product upgrading techniques.

Variables	Items	Frequency	Percent
Did you plant improved tomato varieties?	Yes	119	75.3
	No	39	24.7
Did you apply pesticide?	Yes	103	65.2
	No	55	34.8
Did you stake tomato?	Yes	63	39.9
	No	95	60.1

Source: Own computation from survey result, 2022.

Table 4. Comparison of socioeconomic characteristics of product upgrading participants and non-participants (for continuous explanatory variables).

Discrete variable	Improved tomato variety			Staking			Pesticide application
	Adopter (n = 119)	Non-adopter (n = 39)	t-Value	Adopter (n = 63)	Non-adopter (n = 95)	t-Value	Adopter (n = 103)	Non-adopter (n = 55)	t-Value
Age	41.8	44.7	2.4**	42.02	42.81	0.75	41.39	44.56	2.9***
Fsize	4.2	4.1	−.45	4.4	4.02	−2.3**	4.16	4.19	0.15
Yschool	2.97	1.7	−3.05***	3.06	2.38	−1.85*	2.87	2.24	−1.65*
TLU	6.3	5.4	−2.25**	6.2	5.9	−0.8	6.1	5.9	−0.55
Mrktdis	.83	1.02	4.25***	–	–	–	0.83	0.97	3.4***
Fexp	6.73	5.9	−2.7***	7.09	6.17	−3.35***	6.72	6.2	−1.8*

Note: ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively.

Source: Own computation from survey result, 2022.

Table 5. Comparison in demographic and institutional characteristics of product upgrading participant and non-participant (for dummy variables).

Improved tomato							Pesticide application					Staking
Dummy variables		Adopter (n = 119)		Non-adopter (n = 39)			Adopter (n = 103)		Non-adopter (n = 55)			Adopter (n = 63)		Non-adopter (n = 95)
	Items	N	%	N	%	χ^2-value	N	%	N	%	χ^2-value	N	%	N	%	χ^2-value
Sex	Female	13	11%	10	25.6%	5.1**	15	14.6%	8	14.6%	0.00	6	9.5%	17	17.9%	2.13
	Male	106	89%	29	74.4%		88	85.4%	47	85.4%		57	90.5%	78	82.1%
Offarm	No	57	47.9%	17	43.6%	0.22	49	47.6%	25	45.5%	0.06	29	46%	45	47.4%	0.03
	Yes	62	52.1%	22	56.4%		54	52.4%	30	54.5%		34	54%	50	52.6%
Acctrain	No	70	58.8%	32	82.1%	6.9***	58	56.3%	44	80%	8.79***	29	46%	73	76.8%	15.72***
	Yes	49	41.2%	7	17.9%		45	43.7%	11	20%		–	–	–	–
Accmrkt	No	6	5%	14	35.9%	25.3***	6	5.8%	14	25.5%	12.50***	–	–	–	–	–
	Yes	113	95%	25	64.1%		97	94.2%	41	74.5%		59	93.6%	79	83.2%
Accext	No	34	28.6%	17	43.6%	3.03*	29	28.2%	22	40%	2.30	13	20.6%	38	40%	6.50**
	Yes	85	71.4%	22	56.4%		74	71.8%	33	60%		50	79.4%	57	60%
Acccred	No	65	54.6%	34	87.2%	13.3***	62	60.2%	37	67.3%	0.77	–	–	–	–	–
	Yes	54	45.44%	5	12.8%		41	39.8%	18	32.7%	–	–	–	–	–	–
Accseed	No	10	8.4%	30	76.9%	72.9***	–	–	–	–	–	–	–	–	–	–
	Yes	109	91.6%	9	23.1%	–	–	–	–	–	–	–	–	–	–	–
Accpest	No	–	–	–	–	–	12	11.7%	8	14.5%	0.27	–	–	–	–	–
	Yes	–	–	–	–	–	91	88.3%	47	85.5%		–	–	–	–	–

Note: ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively.

Source: Own computation from survey result, 2022.

Table 6. Tomato variety adopted and their sources.

Tomato variety	Frequency	Percent
Local	39	24.7
Roman VF	84	53.2
Galilea	8	5.1
Golden	27	17.1
Source of seed
Own	17	10.8
Local market	35	22.2
Private seed enterprise	65	41.1
NGOs	8	5.1
OoARD	33	20.9

Source: Own computation from survey result, 2022.

Table 7. Factors in favour of adopting improved seed.

Motive of adopting improved tomato	Responses
	N	Percent
It offers high yield	78	23.0%
It is highly diseases resistance	65	19.2%
It offers better price	105	31.0%
It has high shelf life	91	26.8%

Source: Own computation from survey result, 2022.

Table 8. Sampled households’ participation in product upgrading combination.

Combination of product upgrading techniques	Item	Frequency	Percentage
Staking and Adimtom	Yes	62	39.2
	No	96	60.8
Staking and Appest	Yes	46	29.1
	No	112	70.9
Adimtom and Appest	Yes	83	52.5
	No	75	47.5
Staking, Adimtom, and Appest	Yes	46	29.1
	No	112	70.9

Appest: application of pesticide; Adimtom: adoption of improved tomato.

Source: Own computation from survey result, 2022.

Table 9. Overall fitness, probabilities and correlation matrix of the product upgrading techniques from the MVP model.

Joint probability (success)	0.27
Joint probability (failure)	0.11
Number of draws	5
Observation	158
Log-likelihood	−200.5
Wald chi^2	100.73
Prob > chi^2	0.00***
	ρ1	ρ2	ρ3
ρ1	1.00
ρ2	0.62*** (0.19)	1.00
ρ3	−0.18 (0.15)	0.001 (0.18)	1.00
Likelihood ratio test of rho21 = rho31 = rho32 = 0: chi^2(3) = 9.23 Prob > chi^2 = 0.027

Note: *** indicates statistical significance at 1%: ρ1 = staking, ρ2 = adoption of improved variety, and ρ3 = pesticide application. Parenthesis in the disturbance term correlation matrix showed the standard error.

Source: Own computation from survey result, 2022.

Table 10. Multivariate probit estimations for determinants of producer’s participation decision in product upgrading techniques.

Product upgrading techniques
Variables	Staking		Adimtom		Appest
	Coef. (Std.Err.)	dy/dx	Coef. (Std.Err.)	dy/dx	Coef. (Std.Err.)	dy/dx
Sex	0.195 (0.35)	0.057	0.836 (0.53)	0.114	0.071 (0.35)	0.012
Age	−0.048* (0.03)	−0.014*	−0.016 (0.04)	−0.001	−0.050* (0.03)	−0.015*
Fsize	0.315** (0.15)	0.096**	0.127 (0.27)	0.015	0.113 (0.17)	0.033
Yschool	0.067 (0.05)	0.023	0.197** (0.09)	0.025**	0.061 (0.06)	0.016
Mrktdis	–	–	−0.603 (0.70)	−0.116	−1.209** (0.49)	−0.338**
Fexp	0.124* (0.07)	0.039*	0.205* (0.12)	0.027*	0.136* (0.07)	0.039*
Acctrain	0.753*** (0.23)	0.239***	0.647 (0.42)	0.072	0.483* (0.25)	0.141*
Accmrkt	–	–	1.468*** (0.55)	0.189***	0.936** (0.38)	0.272**
Accseed	–	–	2.004*** (0.4)	0.243***	–	–
Acccred	–	–	0.259 (0.38)	0.040	0.070 (0.24)	0.015
Accext	0.405 (0.25)	0.123	0.023 (0.37)	0.004	0.203 (0.26)	0.071
Accpest	–	–	–	–	1.151** (0.49)	0.343**
TLU	0.041 (0.05)	0.014	0.106 (0.09)	0.012	0.028 (0.05)	0.010
Offarm	−0.069 (0.23)	−0.026	−0.457 (0.35)	−0.054	−0.066 (0.24)	−0.021
_Cons	−1.5 (0.95)	–	−4.154** (1.8)	–	−0.513 (1.27)	–
Predicted probability	0.395		0.760		0.654

Appest: application of pesticide; Adimtom: adoption of improved tomato.

Source: Own computation from survey result, 2022.

Note: ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively.

Table

Type of livestock	Numbers	Type of livestock	Numbers
Ox		Horse
Bull		Donkey
Cow		Donkey (young)
Heifer		Sheep
Calf		Goat
Weaned calf		Sheep/goat (young)
Mule		Chickens
		Others (specify)

Table

Input type	Do you use? 1. Yes 2. No	Source of input (Code A)	Distance to input source (h)	How do you get (Code B)	If not used why?
Seed
Local
Improved
DAP
Urea
Manure
Compost
Pesticides
Farm implements

Table

Code A 1. Own       2. Local market 3. Private seed enterprise 4. Cooperatives (specify) 5. NGOs (specify)  6. Research centre(specify) 7. Jimma University  8. fellow farmers 9. OoARD

Codes B 1. Through purchase on cash 2. On credit 3. Gift 4. Through exchange on kind 5. Own 6. Others (specify)

Table

No	Training type	Do you participated (1. Yes 2. No)	By whom	How many days	Training providers (by whom)
1	Improved variety of tomato				1. OoARD 2. NGOs (specify) 3. Development agents 3. Research centre (specify) 4. Jimma university 5. Cooperative/union (specify) 6. Not know
2	Crop management
3	Fertilizer/compost application
4	Harvesting
5	Post-harvest functional upgrading activities
	1. Washing
	2. Sorting
	3. Grading
	4. Weighing
	5. Processing
	6. Packaging
	7. Transportation
	8. Others____
6	Composition of all

Alwang, J., Barrera, V., Andrango, G., Dominguez, J., Martinez, A., Escudero, L., & Montufar, C. (2019). Value-chains in the Andes: Upgrading for Ecuador’s blackberry producers. Journal of Agricultural Economics, 70(3), 705–730. https://doi.org/10.1111/1477-9552.12329

 Web of Science ®Google Scholar

Nnanna Mba, A., Anyanwu, C. I., & Kalu, U. H. (2015). Factors influencing cassava value addition by rural agribusiness entrepreneurs in Abia State, Nigeria. Crops, 15(3).

 Google Scholar

Adeyonu, A., Ajala, A., Adigun, G., Ajiboye, B., & Gbotosho, O. (2017). Determinants of sweet potato value addition among smallholder farming households in Kwara State, Nigeria. Agro-Science, 15(1), 17–22. https://doi.org/10.4314/as.v15i1.4

 Google Scholar

Doti, A. G. (2018). Determinants of dairy value chain upgrading by smallholder farmers in central Ethiopia. Journal of Dairy Food Sciences, 13, 9–17.

 Google Scholar

Bati, B., & Aman, M. (2017). Determinants of adoption of improved potato varieties by smallholder farmers in Shashemene District, West Arsi Zone, Oromia National Regional State, Ethiopia. Haramaya University.

 Google Scholar

Kinuthia, C. W. (2019). Determinants of pesticide use and uptake of alternative pest control methods among small scale tomato farmers in Nakuru County, Kenya. Egerton University.

 Google Scholar

Molla, E., Hailekirstos, E., Mengstie, M., & Zenebe, T. (2022). Determinants of wheat value chain in case of North Shewa Zone of Amhara region, Ethiopia. Journal of Cogent Economics & Finance, 10(1), 2014639. https://doi.org/10.1080/23322039.2021.2014639

 Google Scholar

Danso-Abbeam, G., Bosiako, J. A., Ehiakpor, D. S., & Mabe, F. N. (2017). Adoption of improved maize variety among farm households in the northern region of Ghana. Cogent Economics & Finance, 5(1), 1416896. https://doi.org/10.1080/23322039.2017.1416896

 Web of Science ®Google Scholar

Tegegne, A. (2017). Value chain analysis of milk and effectiveness of LIVES project intervention towards milk value chain. In Case of Dessie Zuria District, South Wollo zone of Amhara Regional State of Ethiopia. Jimma University.

 Google Scholar

Adunea, D., & Fekadu, B. T. (2019). Adoption determinants of row planting for wheat production in Munesa District of Oromia Region, Ethiopia. Journal of Agricultural Extension and Rural Development, 11(2), 25–34. https://doi.org/10.5897/JAERD2018.0993

 Google Scholar

Khan, M., & Damalas, C. A. (2015). Factors preventing the adoption of alternatives to chemical pest control among Pakistani cotton farmers. International Journal of Pest Management, 61(1), 9–16. https://doi.org/10.1080/09670874.2014.984257

 Web of Science ®Google Scholar