Impact of Improved Agricultural Technology Use on Household Income in Eastern Ethiopia: Empirical Evidence from a Propensity Score Matching Estimation

Abstract

This article investigates the impact of improved agricultural technology use on farm household income in eastern Ethiopia. Primary data for the study were obtained from a random sample of 248 rural households, 119 of which are improved technology users and the rest are non-users. The research employed the propensity score matching procedure to establish the causal relationship between adoption of improved crop and livestock technologies and changes in farm income. Results from the econometric analysis show that households using improved agricultural technologies had, on average, 23,031.28 Birr¹ higher annual farm income compared to those households not using such technologies. Our findings highlight the importance of promoting multiple and complementary agricultural technologies among rural smallholders. Therefore, we suggest that rural technology generation, dissemination and adoption interventions be strengthened. Moreover, the linkage among research, extension, universities and farmers needs to be enhanced through facilitating multistakeholder innovation platforms.

Keywords

Impact agricultural technologies propensity score matching farm income Ethiopia

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References

Abebe

Z. D.

Sewnet

M. A.

(2014). Adoption of soil conservation practices in North Achefer District, Northwest Ethiopia. Chinese Journal of Population Resources and Environment , 12(3), 261–268. https://doi.org/10.1080/10042857.2014.934953

Agricultural Transformation Agency (ATA). (2017). ATA annual report 2017/18. Author. http://www.ata.gov.et/wp-content/uploads/2019/01/ATA_AnnualReport_2010.pdf

Akinola

A. A.

Sofoluwe

N. A.

(2012). Impact of mulching technology adoption on output and net return to yam farmers in Osun State, Nigeria. Agrekon , 51(2), 75–92. https://doi.org/10.1080/03031853.2012.695144

Bahta

Y. T.

Owusu-Sekyere

Tlalang

B. E.

(2018). Assessing participation in homestead food garden programmes, land ownership and their impact on productivity and net returns of smallholder maize producers in South Africa. Agrekon , 57(1), 49–63. https://doi.org/10.1080/03031853.2018.1437051

Bayiyana

Hepelwa

Rao

Mdadila

(2018). Do dairy market hubs improve smallholder farmers’ income? The case of dairy farmers in the Tanga and Morogoro regions of Tanzania. Agrekon , 57(2), 1–16. https://doi.org/10.1080/03031853.2018.1481758

Beegle

Christiaensen

Dabalen

Gaddis

(2016). Poverty in a rising Africa . The World Bank.

Biru

W. D.

Zeller

Loos

T. K.

(2020). The impact of agricultural technologies on poverty and vulnerability of smallholders in Ethiopia: A panel data analysis. Social Indicators Research , 147, 517–544. https://doi.org/10.1007/s11205-019-02166-0

Caliendo

Kopeinig

(2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys , 22(1), 31–72.

(CSA).

Central Statistical Agency.

(2013). Population projection of Ethiopia for all regions at wereda level from 2014–2017 . Author.

10.

Cunguara

Darnhofer

(2011). Assessing the impact of improved agricultural technologies on household income in rural Mozambique. Food Policy , 36(3), 378–390. https://doi.org/10.1016/j.foodpol.2011.03.002

11.

Gezu

Gebisa

Mohammed

Fikadu

Habte

(2019). Effects of intra-row spacing and N fertilizer rates on yield and yield components of tomato (Lycopersicon Esculentum L.) at Harawe, Eastern Ethiopia. Journal of Plant Sciences , 7(1), 8–12. https://doi.org/10.11648/j.jps.20190701.12

12.

Gregory

P. J.

Paul

Shahidur

R. (Eds.)

. (2013). Food and agriculture in Ethiopia—progress and policy challenges. Food Security , 5, 475–476.

13.

Habtemariam

L. T.

Mgeni

C. P.

Mutabazi

K. D.

Sieber

(2019). The fam income and food security implications of adopting fertilizer micro-dosing and tied-ridge technologies under semi-arid environments in central Tanzania. Journal of Arid Environments , 166, 60–67.

14.

Hailu

B. K.

Abrha

B. K.

Weldegiorgis

K. A.

(2014). Adoption and impact of agricultural technologies on farm income: Evidence from southern Tigray, Northern Ethiopia. International Journal of Food and Agricultural Economics , 2(4), 91–106. https://doi.org/10.22004/ag.econ.190816

15.

Houeninvo

G. H.

Quenum

C. V. C.

Nonvide

Armel G. M.

(2019). Impact of improved maize variety adoption on smallholder farmers’ welfare in Benin. Economics of Innovation and New Technology , 29(8), 831–846. https://doi.org/10.1080/10438599.2019.1669331

16.

Jama

Kuntashula

Samboko

P. C.

(2019). Adoption and impact of the improved fallow technique on cotton productivity and income in Zambia. Sustainable Agriculture Research , 8(2), 1–19. https://doi.org/10.5539/sar.v8n2p1

17.

Kabunga

N. S.

Dubois

Qaim

(2014). Impact of tissue culture banana technology on farm household income and food security in Kenya. Food Policy , 45, 25–34. https://doi.org/10.1016/j.foodpol.2013.12.009

18.

Kassie

Shiferaw

Muricho

(2011). Agricultural technology, crop income, and poverty alleviation in Uganda. World Development , 39(10), 1784–1795. https://doi.org/10.1016/j.worlddev.2011.04.023

19.

Khandker

S. R.

Koolwal

G. B.

Samad

H. A.

(2010). Handbook on impact evaluation: Quantitative methods and practices . World Bank Publications.

20.

Khonje

Manda

Alene

A. D.

Kassie

(2015). Analysis of Adoption and Impacts of Improved Maize Varieties in Eastern Zambia. World Development , 66, 695–706. https://doi.org/10.1016/j.worlddev.2014.09.008

21.

Makate

Wang

Makate

Mango

(2017). Impact of drought tolerant maize adoption on maize productivity, sales and consumption in rural Zimbabwe. Agrekon , 56(1), 67–81. https://doi.org/10.1080/03031853.2017.1283241

22.

Mengistu

Degefu

(2017). Adoption intensity of inorganic fertilizers in maize production: Empirical evidence from smallholder farmers in Eastern Ethiopia. Journal of Agricultural Science , 9(5), 124–132. https://doi.org/10.5539/jas.v9n5p124

23.

Mignouna

D. B.

Manyong

V. M.

Rusike

Mutabazi

K. D. S.

Senkondo

E. M.

(2011). Determinants of adopting Imazapyr-resistant maize technologies and its impact on household income in western Kenya. AgBioForum , 14(3), 158–163.

24.

Nigussie

Kibebew

Bosena

Tesfaye

Mengistu

Feyisa

(2018). Innovations in technology, institutional and extension approaches towards sustainable agriculture and enhanced food and nutrition security in Africa . European Commission.

25.

Rosenbaum

P. R.

Rubin

D. B.

(1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician , 39(1), 33–38. https://doi.org/10.2307/2683903

26.

Sianesi

(2004). An evaluation of the Swedish system of active labour market programs in the 1990s. Review of Economics and Statistics , 86(1), 133–155. https://doi.org/10.1162/003465304323023723

27.

Tayib

Mezene

Tadesse

Adem

(2015). Camel trypanosomiasis in Babile district, Eastern Hararghe Zone, Oromia Regional State, Eastern Ethiopia. Academic Journal of Animal Diseases , 4(3), 141–145.

28.

Teklewold

Kassie

Shiferaw

Köhlin

(2013). Cropping system diversification, conservation tillage and modern seed adoption in Ethiopia: Impacts on household income, agrochemical use and demand for labour. Ecological Economics , 93, 85–93. https://doi.org/10.1016/j.ecolecon.2013.05.002

29.

Tesfaye

Bedada

Mesay

(2016). Impact of improved wheat technology adoption on productivity and income in Ethiopia. African Crop Science Journal , 24(1), 127–135. https://doi.org/10.4314/acsj.v24i1.14S

30.

Wake

R. D.

Habteyesus

D. G.

(2019). Impact of high yielding wheat varieties adoption on farm income of smallholder farmers in Ethiopia. International Journal of Agricultural Extension , 07(01), 45–59. https://doi.org/10.33687/ijae.007.01.2490

31.

Wordofa

M. G.

Sassi

(2018). Impact of farmers’ training centers on household income: Evidence from propensity score matching in Eastern Ethiopia. Social Sciences , 7(1), 1–12. https://doi.org/10.3390/socsci7010004