Impacts of grassroots public agricultural technology promotion on farmers’ technology adoption: an empirical analysis of rice technology demonstration

TONG Dajian1 HUANG Wu2 Ying Ruiyao1

(1.School of Economics and Management, Nanjing Agricultural University)
(2.School of Humanities & Social Development, Nanjing Agricultural University)

【Abstract】This article analyzes the impacts of promotion of grassroots public agricultural technology (GPATE) on farmers’ technology adoption based on survey data from 994 rice farmers in 11 provinces. It uses the propensity score matching to overcome the potential endogeneity of technology promotion. The results indicate that GPATE promotes technology adoption to some extent, generates partial spillover effects and different benefits for farmers with varied scales of operation. Compared to farmers in non-demonstration villages, GPATE significantly promotes the adoption behaviors of demonstration households in testing soil for formulated fertilization, straw returning and green-controlling of pests and diseases. It also significantly promotes the adoption behaviors of non-demonstration households in demonstration villages in testing soil for formulated fertilization and straw returning, yet without significant impacts on using green-controlling of pests and diseases technology. Moreover, GPATE significantly promotes technology adoption behaviors of small-scale farmers, but it has no significantly positive impact for large-scale farmers. Future policies should work to enhance spillover effects of GPATE, give classified guidance for different technologies, avoid overemphasizing scale operation in selecting promotion targets, and give more attention to small-scale farmers.

【Keywords】 promotion of grassroots public agricultural technology; technology adoption; spillover effect; allocation effect;


【Funds】 project of National Social Science Fund (16BGL124) project of National Natural Science Foundation (71773050) project of Humanities and Social Sciences Foundation of the Fundamental Research Funds for the Central Universities of Nanjing Agricultural University (SKPT2016016)

Download this article


    [1]. ① As the probability of technology spillovers from the non-demonstration village farmers is very low, the interference of technology spillover can be eliminated by using them as the control group. [^Back]

    [2]. ① The propensity score estimation is based on the sample of demonstration villages, which can better identify the allocation of promotion resources among the farmers, because in fact, only in the villages where science and technology demonstration is carried out can the farmers have the opportunity to become demonstration households. [^Back]

    [3]. ① In the sample, the scale of farmer household management is unevenly distributed and the standard deviation is very large. This paper divides farmer households into ten mu management scale, in order to better balance the sub-sample size of treatment group and non-treatment group. [^Back]


    1. Chen, Z., Li, H., Liu, X. et al. Forward Position or Economics (产经评论), (3) (2015).

    2. Chu, C., Feng, S. & Zhang, W. Chinese Rural Economy (中国农村经济), (3) (2012).

    3. Feng, X. China Rural Survey (中国农村观察), (2) (2015).

    4. Gao, Y., Wang, N., Li, X. et al. Issues in Agricultural Economy (农业经济问题), (1) (2017).

    5. Hua, C., Lu, Q., Jiang, Y. et al. Journal of Agrotechnical Economics (农业技术经济), (4) (2013).

    6. Huang, J., Hu, R. & Zhi, H. Journal of Agrotechnical Economics (农业技术经济), (1) (2009).

    7. Jiang, C. Theoretical Investigation (理论探讨), (1) (2015).

    8. Sun, X. Journal of Northwest A&F University (Social Science Edition) (西北农林科技大学学报(社会科学版)), (2) (2017).

    9. Wen, C. & Wu, J. Journal of China Agricultural University (中国农业大学学报), (9) (2016).

    10. Ying, R. & Zhu, Y. China Rural Survey (中国农村观察), (1) (2015).

    11. Yu, Y. & Zhang, J. Chinese Rural Economy (中国农村经济), (11) (2009).

    12. Biggs, S., and G. Smith, 1998, “Beyond Methodologies: Coalition-building Participatory Technology Development”, World Development, 26 (2): 238–239.

    13. Cleaver, F., 1999, “Paradoxes of Participation: Questioning Participatory Approaches to Development”, Journal of International Development, 11 (4): 597–612.

    14. Cunguara, B., and I. Darnhofer, 2011, “Assessing the Impact of Improved Agricultural Technologies on Household Income in Rural Mozambique”, Food Policy, 36 (3): 378–390.

    15. Emmanuel, D., E. Owusu-Sekyere, V. Owusu, and H. Jordaan, 2016, “Impact of Agricultural Promotion Service on Adoption of Chemical Fertilizer: Implications for Rice Productivity and Development in Ghana”, NJAS-Wageningen Journal of Life Sciences, 79 (11): 41–49.

    16. Evenson, R., 1997, Improving Agricultural Promotion: A Reference Manual, Rome: FAO.

    17. Feder, G., R. R. Murgai, and J. B. Quizon, 2004, “The Acquisition and Diffusion of Knowledge: The Case of Pest Management Training in Farmer Field Schools, Indonesia”, Journal of Agricultural Economics, 55 (2): 221–243.

    18. Feder, G., and R. Slade, 1986, “A Comparative Analysis of Some Aspects of the Training and Visit System of Agricultural Promotion in India”, Journal of Development Studies, 22 (2): 407–428.

    19. Heckman, J. J., H. Ichimura, and P. E. Todd, 1997, “Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Program”, Review of Economic Studies, 64 (4): 605–654.

    20. Hoang, L. A., J. Castella, and P. Novosad, 2006, “Social Networks and Information Access: Implications for Agricultural Promotion in a Rice Farming Community in Northern Vietnam”, Agriculture and Human Values, 23 (4): 513–527.

    21. Hu, R. F., Y. Q. Cai, K. Z. Chen, and J. K. Huang, 2012, “Effects of Inclusive Public Agricultural Promotion Service: Results from a Policy Reform Experiment in Western China”, China Economic Review, 23 (4): 962–974.

    22. Hujer, R., M. Caliendo, and S. L. Thomsen, 2004, “New Evidence on the Effects of Job Creation Schemes in Germany: A Matching Approach with Threefold Heterogeneity”, Research in Economics, 58 (4): 257–302.

    23. Kondylis, F., V. Mueller, and J. Zhu, 2017, “Seeing is Believing? Evidence from an Promotion Network Experiment”, Journal of Development Economics, 125 (2): 7–20.

    24. Rosenbaum, P. R., and D. B. Rubin, 1983, “The Central Role of the Propensity Score in Observational Studies for Causal Effects”, Biometrika, 70 (1): 41–55.

    25. Rosenbaum, P. R., and D. B. Rubin, 1985, “Constructing Control Group Using a Multivariate Matched Sampling Method that Incorporates the Propensity Score”, American Statistician, 39 (1): 33–38.

    26. Rosenbaum P. R., 2002, Observational Studies, New York: Springer.

    27. Tripp, R., M. Wijeratne, and V. H. Piyadasa, 2005, “What Should We Expect from Farmer Field Schools? A Sri Lanka Case Study”, World Development, 33 (10): 1705–1720.

This Article


CN: 11-3586/F

Vol , No. 04, Pages 59-73

July 2018


Article Outline


  • 1 Introduction
  • 2 Analytical framework and econometric model
  • 3 Data sources, variable selection and descriptive statistics
  • 4 Empirical results and analysis
  • 5 Conclusion and enlightenment
  • Footnote