Adoption and impact of improved wheat varieties on productivity and welfare among smallholder farmers in the Arsi Highland of Ethiopia

ABSTRACT

This article evaluates the adoption and impact of improved wheat varieties on rural farm household welfare measured by consumption expenditure per adult equivalent and productivity per hectare in rural Ethiopia. The study utilises cross-sectional farm household-level data collected in 2017/2018 from a randomly selected sample of 323 farmers in Arsi Highland of Ethiopia. We estimate the adoption and causal impact of improved varieties by utilising endogenous switching regression complemented with a binary propensity score matching methodology. This helps us estimate the productivity and welfare effect of technological adoption by controlling for the role of selection bias problem stemming from both observed and unobserved heterogeneity. Our analysis reveals a consistent result across models indicating that adoption enhances wheat productivity per hectare by 0.63 tons/ha and household welfare by 31%. Even farm households that did not adopt would benefit significantly had they adopted. Education, wheat price, farm machineries, crop rotation, row planting, social capital (such as informal network, core trust, and institutional trust), training on varieties selection, and information on seed availability are found to be the main drivers behind the adoption of improved wheat varieties.

KEYWORDS:

• Technology adoption
• endogenous switching regression
• propensity score matching
• productivity
• household welfare

Acknowledgements

The authors would like to thank Arsi University, College of Agriculture and Environmental Science for the Ph.D. financial support to this study. The authors are grateful to the District Agricultural Development Office staff members, especially those located in the survey districts. Besides, the contribution of all the survey respondents, enumerators, and staff member of Agricultural Economics is hereby acknowledged. The authors are also grateful to two anonymous reviewers and the journal editors for their constructive comments on previous versions of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 However, progressive farm households under optimum conditions in the study area could harvest up to 8 tons/ha indicating up to 338% yield gap.

2 This refers to the smallest administrative unit in the country.

3 Proportion to size sampling approach is specified as follows: $n=\left(z^{2}PQ/d^2\right)$ where n is the sample size, P is the proportion of farmers growing improved varieties in the study area which is stated based on adoption rates of 70% (Shiferaw et al. 2014), P is set at 0.70. The variable d is significant level at 5%, this also leads to a z-value of 1.96 and Q is the weighting variable and it is computed as 1−P.

4 We employed the standard conversion factor based on the “OECD-modified adult equivalent scale” which is given by 1 + 0.5 (A-1) + 0.3C, where A and C represent the number of adult and children in a household, respectively.

5 The three years cut-off point was decided in consultation with wheat breeders and farmers.

6 The falsification test on the selection instruments variables shows that they are jointly statistically significant in the adoption decision (in selection equation: chi²=  26.82; p-value = 0.0001), but not in the consumption expenditure per AEU (in outcome equation: F-stat = 1.23; p-value = 0.2973) and productivity per hectare (in outcome equation: F-stat = 1.42; p-value = 0.2176).

7 Two caliper scales were used: 0.1 and 0.5.

8 The Epanechnikov kernel estimator with 0.03 and 0.06 bandwidth were used.

9 Farm households use different rotation practices such as legume-wheat, potato-wheat, maize-wheat or oil crops-wheat rotation systems.

10 The exact definition of social capital is subject of debate, even though according to Adler and Kwon (2002) it is the goodwill available to individuals or groups developed through social interaction.

11 Include the cost for pesticide and herbicide (such as Topik, Palase, 2, 4-D, Gran star), insecticide (such as Karate), and fungicide (such as Tilt®250 E.C).

12 1 USD = ETB 27 during the survey period.

13 Include the cost for pesticide and herbicide (such as Topik, Palase, 2, 4-D, Gran star), insecticide (such as Karate), and fungicide (such as Tilt®250 E.C).

14 In most empirical studies a mean standardised bias below 3% or 5% after matching is seen as sufficient (Caliendo and Kopeinig 2008).

Additional information

Funding

This work was supported by the Arsi University. College of Agriculture and Environmental Science, School of Graduate Study.