Document Type: Original Article


1 University of Tabriz , Faculty of Agriculture, Agricultural Economics Department, Tabriz, Iran

2 University of Tabriz, Faculty of Agriculture, Agricultural Economics Department, Tabriz, Iran

3 University of Tabriz, Faculty of Agriculture, Department of Agricultural Economics, Tabriz, Iran


This study identifies and analyzes factors influencing canola plantation development in Tabriz and Marand Counties. The Censored Model was used to analyze cross-sectional data collected from 372 farmers using a questionnaire. Due to the weakness of the Tobit Model in separating factors affecting the adoption decision of farmers and factors affecting the rate of adoption, the Heckman Model was employed to separate the contributions made by these factors. The results of Estimated Probit Model in the first stage of the Heckman Approach showed that machinery ownership had an important effect on canola adoption, as a 1% increase in machinery ownership had led to 0.158% increase in canola adoption probability. Contact with extension agents, farm income proportion, education, and farmers’ experience influenced canola plantation probability positively, and the age and number of fragmentations had a negative impact on it. The significance of inverse Mill’s ratio indicates that the factors affecting the decision to start planting and the amount of canola plantation are not the same. The Heckman’s second step estimation results indicated that the loan amount, canola relative benefit, and family labor had a positive effect, and that machinery cost and farm distance from the road had a negative effect on canola acreage. Relative benefit was the most effective element, as 1% increase in relative benefit results in a 0.342% increase in canola plantation.

Graphical Abstract


Domestic production of oil is insufficient to meet demand of Iran’s population. Canola is one of important oilseeds that contain a high percent of oil.

Despite the attempt made to increase canola production, East Azerbaijan province has experienced considerable variation in canola cultivation.

Machinery cost and relative benefit of canola were the most important factors that could influence canola adoption probability and cultivated area.



Main Subjects

Adesina, A. A., &Zinnah, M. M. (1993). Technology characteristics, farmers' perceptions and adoption decisions: A TobitModel application in Sierra Leone. Agricultural Economics, 9(4), 297-311.

Abyar, N.)2002(. A study of factors influencing soy bean area expansion in GolestanProvince. Journal of Agricultural Economics and Development, 10(38), 67-82.

Amao, J. O.,&Awoyemi, T. T. (2008). Adoption of improved cassava varieties and its welfare effect on producing households in OsogboAdp zone of OsunState. Gene Conserve, 7(29), 1-11    

Ben-Houassa, K. E. (2011). Adoption and levels of demand of fertilizer in cocoa farming in Cote D’ivoire: does risk aversion matters? Paper selected for presentation at the CSAE conference, economic development in Africa.

Caviglia, J. L., & Khan, J. R. (2001). Diffusion of sustainable agriculture in the Brazilian tropical rain forests: a discrete choice analysis. Economic Development and Cultural Change, 49(2), 311-333.

East Azerbaijan Province Organization of Jiha­d- Agriculture. (2010). Agricultural Statistics Year Book, Department of Planning and Economics, Tabriz, Iran.

Foster, A. D., &Rosenzweig, M. R. (1995). Learning by doing and learning from others: human capital and technical change in agriculture. Journal of Political Economy, 103(6), 1176-1209.

Gebremedhin, B., Ahmed, M.,& Ehui, S. K. (2003). Determinants of adoption of improved forage technologies in crop-livestock mixed systems: evidence from the highlands of Ethiopia. Tropical Grasslands, 37, 262-273. 

Greene, W. H. (1990). Econometric analysis (2ndEds). New York: Macmillan publishing company, USA.

Heckman, J. J. (1979). Sample selection bias as a spesification error. Econometrica, 47(1), 153- 161.

Ibrahim, M., Florkowski, W. J.,& Kolavalli, S. (2012). The determinants of farmer adoption of improved peanut varieties and their impact on farm income: evidence from Northern Ghana. Selected peper prepared for presentation at the agricultural and applied economics association annual meeting, Seattle,WA.

Kheil,  A., Saint-Macary, C.,& Zeller, M. (2009). Maize boom in the uplands of northern Vietnam, economic importance  and environmental implication. In: International research on food security, natural resource management and rural development conference, PP. 1-6.

Kinuthia, E. K., Owuor, G., Nguyo, W., Kalio, A. M.,& Kinambuga, D. (2011). Factors influencing participation and acreage allocation in tree planting program: a case of Nyeri district, Kenya. Agricultural Science Research Journal,1(6), 129-133.

Lohr, L., & Park, T.A. (1995). Utility-consistent discrete continuous choices in soil conservation. Land Economics, 71(4), 474-490.

Lubell, M. (2004). Collaborative watershed management: a view from the grassroots. Policy Studies Journal, 32(3), 341-361.

Mostofi, S. (2008). Investigation of oilseed and its products. Agricultural Planning, Economic and Rural Development Research Institute Tehran,1-50.

Munshi, K. (2004). Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution. Journal of Development Economics, 73(1), 185-213.

McDonald, J.,&Moffit, R. (1982). The uses of Tobit analysis.The Review of Economics and Statistics, 62(2), 318-321.

Ngwira, A., Johnsen, F. H., Aune, J. B., Mekuria, M.,&Thierfelder, C. (2014).Adoption and extent of conservation agriculture practices among smallholder farmers in Malawi. Journal of  Soil and Water Conservation, 69(2), 107-119.

Oladele, O. I. (2005). A Tobit analysis of propensity to discontinue adoption of agricultural technology among farmers in southwestern Nigeria. Journal of Central European    Agriculture, 6(3), 249-254.

Oyekale, A. S. &Idjesa, E. (2009). Adoption of improved maize seeds and production efficiency in Rivers state, Nigeria. Academic Journal of Plant Sciences, 2(1), 44-50.

Rogers, E. (1962). Diffusion of innovation (5thEds). New York: Free Press.

Salami, H.,&Einallahi Ahmad Abadi, M. (2001). Application of Tobit econometric model and the two-stage Heckman method in determining factors affecting sugar beet production in Khorasan province. Iranian Agricultural Sciences, 32(2), 433-445.

Scandizzo, P. L.,&Savastano, S. (2010). The adoption and diffusion of GM crops in United States: a real option approach. AgBio Forum, 13(2), 142-157.

Shafiei, L. (2007). Studying effective factors on olive cultivation in Kerman. Journal of Agricultural Economics and Development, 15(58), 1-22.

Shapiro, B. I., Brorsen, B. W. & Doster, D. H. (1992). Adoption of double-cropping soybeans and wheat. Southern Journal of Agricultural Economics, 24(02), 33-40.

Sigleman, L. & Zeng, L. (1999). Analysing censored and sample selected data with tobit and Heckit models. Political Analysis, 8(2), 167-82.

Taheri, F., Mousavi, S. N.,&Rezayie, M. R. (2010). The impact of removing energy subsidies on rapeseed production costs in MarvdashtCounty. Journal of Agricultural Economics Research,    2(3), 77-89.

Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica,  26(1), 24-36.

Wright, M., &Charlert, D. (1995). New product diffusion models in marketing: an assessment of two approaches. Marketing Bulletin, 6(4), 32-41.

Youssefi, A., Nshauian, A.,&Azizi, M. (2010). Yield differences of brassica oilseed rape cultivars based on physiological parameters. American-Eurasian Journal of Agricultural & Environmental Sciences, 9(4), 436-439