Adoption Pattern and Welfare Impact of Agricultural Technology

Abstract

This article examines the adoption of new-generation modern rice varieties (MRVs) and their impact on family welfare among rural farm households in central Nepal. Using cross-sectional data from 416 farm households and the Heckman’s sample selection model, this article finds a positive and significant impact of MRVs on farm income; distance to market and off-farm work exert a negative effect on farm income. The likelihood of adoption is mainly explained by age and education of the household head, access to extension services, seed access and acceptability of the rice grain to consumers. The results further reveal that farm size and land type have significant and positive influences on the adoption of MRVs. Designing mechanisms to help promote MRVs’ adoption among poorer households by developing new varieties suited for rain-fed areas may be a reasonable policy instrument to increase farm incomes. The results of this study suggest that because of the difficulties and costs of transportation and travel to distant markets, availability of seed in local retail outlets will benefit the farmers. To reduce transaction costs, farmers need to be linked to markets by a wide seed distribution network. Integrating farmers with potential buyers may increase profitability in agriculture and its subsequent impacts on rural farmers’ food security and general livelihood.

Keywords

Technology adoption modern rice varieties farm income food security Nepal South Asia

References

Abebaw

Haile

M. G.

(2013). The impact of cooperatives on agricultural technology adoption: Empirical evidence from Ethiopia. Food Policy, 38, 82–91. doi:10.1016/j.foodpol.2012.10.003

Amsalu

De Graaff

(2007). Determinants of adoption and continued use of stone terraces for soil and water conservation in an Ethiopian highland watershed. Ecological Economics, 61(2), 294–302.

Asfaw

Shiferaw

Simtowe

Lipper

(2012). Impact of modern agricultural technologies on smallholder welfare: Evidence from Tanzania and Ethiopia. Food Policy, 37(3), 283–295. doi:10.1016/j.foodpol.2012.02.013

Becerril

Abdulai

(2010). The impact of improved maize varieties on poverty in Mexico: A propensity score-matching approach. World Development, 38(7), 1024–1035. doi:10.1016/j.worlddev.2009.11.017

Bekele

Drake

(2003). Soil and water conservation decision behavior of subsistence farmers in the Eastern Highlands of Ethiopia: A case study of the Hunde-Lafto area. Ecological Economics, 46(3), 437–451.

Bola

A. A.

Diagne

Wiredu

A. N.

Ojehomon

V. E.

(2012). Wealth status and agricultural technology adoption among smallholder rice fermers in Nigeria. International Journal of Sustainable Development, 5(2), 97–114.

CBS (Central Bureau of Statistics). (2011). Nepal living standard survey (Vol. 3). National Planning Commission Secretariat, Kathmandu, Nepal: Government of Nepal.

Chang

H.-H.

Mishra

(2008). Impact of off-farm labor supply on food expenditures of the farm household. Food Policy, 33(6), 657–664. doi:10.1016/j.foodpol.2008.02.002

Cunguara

Darnhofer

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

10.

Doss

C. R.

(2001). Designing agricultural technology for African women farmers: Lessons from 25 years of experience. World Development, 29(12), 2075–2092. doi: https://dx-doi-org.web.bisu.edu.cn/10.1016/S0305-750X(01)00088-2

11.

Doss

C. R.

(2006). Analyzing technology adoption using microstudies: Limitations, challenges, and opportunities for improvement. Agricultural Economics, 34(3), 207–219.

12.

FAOSTAT (Food and Agriculture Organization of the United Nations Statistic Division). (2013). FAOSTAT database. Retrieved on 28 October 2014, from http://faostat3.fao.org/compare/E

13.

Feleke

Zegeye

(2006). Adoption of improved maize varieties in Southern Ethiopia: Factors and strategy options. Food Policy, 31(5), 442–457. doi:10.1016/j.foodpol.2005.12.003

14.

Filmer

Pritchett

L. H.

(2001). Estimating wealth effects without expenditure data—or tears: An application to educational enrollments in states of India. Demography, 38(1), 115–132.

15.

Foster

A. D.

Rosenzweig

M. R.

(2010). Microeconomics of technology adoption. Annual Review of Economics, 2(1), 395–424.

16.

Gauchan

Panta

H. K.

Gautam

Nepali

M. B.

(2012). Patterns of adoption of improved rice varieties and farm-level impacts in stress-prone rainfed areas of Nepal. In Pandey

Gauchan

Malabayabas

Bool-Emerick

Hardy

(Eds), Patterns of adoption of improved rice varieties and farm-level impacts in stress-prone rainfed areas in South Asia (pp. 37–103). Los Baños, Philippines: International Rice Research Institute.

17.

Greene

W. H.

(Ed.). (2012). Econometric analysis. New York University: Prentice Hall.

18.

Gumma

M. K.

Gauchan

Nelson

Pandey

Rala

(2011). Temporal changes in rice-growing area and their impact on livelihood over a decade: A case study of Nepal. Agriculture, Ecosystems & Environment, 142(3–4), 382–392. doi: https://dx-doi-org.web.bisu.edu.cn/10.1016/j.agee.2011.06.010

19.

Hair

J. F.

Tatham

R. L.

Anderson

R. E.

Black

(2006). Multivariate data analysis (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall.

20.

Heckman

J. J.

(1979). Sample selection bias as a specification error. Econometrica: Journal of the Econometric Society, 47(1), 153–161.

21.

Hoffmann

Kassouf

A. L.

(2005). Deriving conditional and unconditional marginal effects in log earnings equations estimated by Heckman’s procedure. Applied Economics, 37(11), 1303–1311. doi:10.1080/00036840500118614

22.

Huang

Rozelle

(2009). Moving off the farm and intensifying agricultural production in Shandong: A case study of rural labor market linkages in China. Agricultural Economics, 40(2), 203–218. doi:10.1111/j.1574-0862.2009.00370.x

23.

Irfan

(2011). Modeling wages of females in the UK. International Journal of Business and Social Science, 2(11), 195–202.

24.

Joshi

K. D.

Conroy

Witcombe

J. R.

(2012, December). Agriculture, seed, and innovation in Nepal: Industry and policy issues for the future (IFPRI Project Paper). Washington DC: International Food Policy Research Institute.

25.

Kassie

Shiferaw

Muricho

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

26.

Khonje

Manda

Alene

A. D.

Kassie

(2015). Analysis of adoption and impacts of improved maize varieties in Eastern Zambia. World Development, 66, 695–706. doi:10.1016/j.worlddev.2014.09.008

27.

Kolenikov

Angeles

(2009). Socioeconimic status measurement with discrete proxy variables: Is principal compoent analysisa reliable answer? Review of Income and Wealth, 55(1), 128–165. doi:10.1111/j.1475-4991.2008.00309.x

28.

Kuhlgatz

Abdulai

(2011). Determinants and welfare impacts of export crop cultivation—Empirical evidence from Ghana. Paper presented at the EAAE International Congress on Change and Uncertainty: Challenges for Agriculture, Food and Natural Resources, 30 August to 2 September 2011, ETH Zurich, Zurich, Switzerland.

29.

Langyintuo

A. S.

Mungoma

(2008). The effect of household wealth on the adoption of improved maize varieties in Zambia. Food Policy, 33(6), 550–559. doi:10.1016/j.foodpol.2008.04.002

30.

Langyintuo

A. S.

Mwangi

Diallo

A. O.

MacRobert

Dixon

Bänziger

(2010). Challenges of the maize seed industry in eastern and southern Africa: A compelling case for private–public intervention to promote growth. Food Policy, 35(4), 323–331. doi:10.1016/j.foodpol.2010.01.005

31.

Mariano

M. J.

Villano

Fleming

(2012). Factors influencing farmers’ adoption of modern rice technologies and good management practices in the Philippines. Agricultural Systems, 110, 41–53. doi:10.1016/j.agsy.2012.03.010

32.

Mendola

(2007). Agricultural technology adoption and poverty reduction: A propensity-score matching analysis for rural Bangladesh. Food Policy, 32(3), 372–393. doi:10.1016/j.foodpol.2006.07.003

33.

Minten

Barrett

C. B.

(2008). Agricultural technology, productivity, and poverty in Madagascar. World Development, 36(5), 797–822. doi:10.1016/j.worlddev.2007.05.004

34.

Mishra

A. K.

Sandretto

C. L.

(2002). Stability of farm income and the role of nonfarm income in US agriculture. Applied Economic Perspectives and Policy, 24(1), 208–221. doi:10.1111/1058-7195.00014

35.

Mittal

Kumar

(2000). Literacy, technology adoption, factor demand and productivity: An econometric analysis. Indian Journal of Agricultural Economics, 55(3), 490–499.

36.

MoAD (Ministry of Agricultural Development). (2013). Statistical Information on Nepalese Agriculture. Agri-Business Promotion and Statistics Division, Kathmandu, Nepal: Government of Nepal.

37.

Mugonolaa

Deckersa

Poesena

Isabiryec

Mathijsa

(2013). Adoption of soil and water conservation technologies in the Rwizi catchment of south western Uganda. International Journal of Agricultural Sustainability, 11(3), 264–281. doi:10.1080/14735903.2012.744906

38.

NARC (Nepal Agriculture Research Council). (2014). Released and registered crop varieties in Nepal (1960–2013) (Vol. 0040-2013/14). Khumaltar, Nepal: Communication, Publication and Documentation Division, NARC.

39.

Nkamleu

G. B.

Adesina

A. A.

(2000). Determinants of chemical input use in peri-urban lowland systems: Bivariate probit analysis in Cameroon. Agricultural Systems, 63(2), 111–121. doi: https://dx-doi-org.web.bisu.edu.cn/10.1016/S0308-521X(99)00074-8

40.

Noltze

Schwarze

Qaim

(2013). Impacts of natural resource management technologies on agricultural yield and household income: The system of rice intensification in Timor Leste. Ecological Economics, 85, 59–68. doi: https://dx-doi-org.web.bisu.edu.cn/10.1016/j.ecolecon.2012.10.009

41.

Pant

B. D.

(2015, February 13). Accelerating growth in agricultural productivity and profitability. Paper presented at Regional Workshop on Agricultural Transformation: Challenges and Opportunities in South Asia, Kathmandu, Nepal.

42.

Paudel

Matsuoka

(2008). Factors influencing adoption of improved maize varieties in Nepal: A case study of Chitwan District. Australian Journal of Basic and Applied Sciences, 2(4), 823–834.

43.

Pfeiffer

López-Feldman

Taylor

J. E.

(2009). Is off-farm income reforming the farm? Evidence from Mexico. Agricultural Economics, 40(2), 125–138. doi:10.1111/j.1574-0862.2009.00365.x

44.

Polson

R. A.

Spencer

D. S.

(1991). The technology adoption process in subsistence agriculture: The case of cassava in Southwestern Nigeria. Agricultural Systems, 36(1), 65–78. doi:10.1016/0308-521X(91)90108-M

45.

Ransom

J. K.

Paudyal

Adhikari

(2003). Adoption of improved maize varieties in the hills of Nepal. Agricultural Economics, 29(3), 299–305. doi:10.1111/j.1574-0862.2003.tb00166.x

46.

Raut

Sitaula

K. B.

Vatn

Paudel

S. G.

(2011). Determinants of adoption and extent of agricultural intensification in the central mid-hills of Nepal. Journal of Sustainable Development, 4(4), 47–60. doi:10.5539/jsd.v4n4p47

47.

Smale

Just

R. E.

Leathers

H. D.

(1994). Land allocation in HYV adoption models: An investigation of alternative explanations. American Journal of Agricultural Economics, 76(3), 535–546. doi:10.2307/1243664

48.

Sunding

Zilberman

(2001). The agricultural innovation process: research and technology adoption in a changing agricultural sector. Handbook of Agricultural Economics, 1, 207–261.

49.

Takahashi

Barrett

C. B.

(2014). The system of rice intensification and its impacts on household income and child schooling: Evidence from rural Indonesia. American Journal of Agricultural Economics, 96(1), 269–289. doi:10.1093/ajae/aat086

50.

Uaiene

R. N.

Arndt

Masters

(2009). Determinants of agricultural technology adoption in Mozambique (Discussion Paper No. 67E). Republic of Mozambique: Ministry of Planning and Development.

51.

Umali-Deininger

(1997). Public and private agricultural extension: Partners or rivals? The World Bank Research Observer, 12(2), 203–224. doi:10.1093/wbro/12.2.203

52.

Vella

Verbeek

(1999). Estimating and interpreting models with endogenous treatment effects. Journal of Business & Economic Statistics, 17(4), 473–478. doi:10.1080/07350015.1999.10524835

53.

Wooldridge

J. M.

(Ed.). (2010). Econometric analysis of cross section and panel data. Cambridge, USA: MIT press.

54.

Wozniak

G. D.

(1984). The adoption of interrelated innovations: A human capital approach. The Review of Economics and Statistics, 66(1), 70–79.

55.

Ding

Pandey

Tao

(2010). Assessing the impact of agricultural technology adoption on farmers’ well-being using propensity-score matching analysis in rural China. Asian Economic Journal, 24(2), 141–160. doi:10.1111/j.1467-8381.2010.02033.x