Main Article Content
Abstract
A very important breakthrough in saffron cultivation and production was achieved by Sher-e- Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K) when the university developed a production system module in saffron which brought substantial increase in productivity of saffron during last two decades. The adoption of the technology was observed to have a very significant impact on the social dynamics of the saffron producing region demanding its ex-ante and ex-post evaluation vis a vis non adopters of the technology. With this in mind consumer surplus model and propensity score matching methods were employed on a sample of 447 respondents of which 286 were adopters and 161 non-adopters (control group) drawn from a population of 753 saffron growers in the saffron belt of Jammu and Kashmir producing 99% of the total saffron production in the country. The results revealed that average productivity of the spice increased from 2.57 kg.ha-1 to 6.05 kg.ha-1, with 1-2 kg.ha-1 in the first year to 10-12 kg.ha-1 in fourth year against control group, however, the investment cost estimates recorded increase of 5.9% under ex-ante and 13.6% under ex-post evaluation while adopting new technology, which however, got compensated through realizing higher productivity and increased employment to the tune of 40.6 and 28.3 per cent man-days/ha respectively under ex-ante and ex-post evaluation. The results further revealed, NPV, BCR, IRR of Rs. 399 crores, 110, 154% against Rs.249 crores, 69, 134% respectively under ex-ante and ex-post evaluation of the technology.
Keywords
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
- Adewale A. J., (1993). An assessment of the impact of Farming Systems Research/Extension on the adoption of agricultural technologies in the Middle-Belt region of Nigeria, Retrospective Theses and Dissertations. 10399.
- Alston, J. M., Craig, B. J., & Pardey, P. G. (1998). Dynamics in the creation and depreciation of knowledge, and the returns to research. Discuss. Pap. 35, EPTD, International Food Policy Research Institute. https://core.ac.uk/download/pdf/194595032.pdf
- Alston J. M., Edwards G. W, Freebairn J. W., (1988). Market distortions and the benefits from research. American Journal of Agricultural Economics, 70, 281-288. https://ideas.repec.org/a/oup/ajagec/v70y1988i2p281-288..html
- Alston, J. M., Chan-Kang, C., Marra, M. C., Pardey, P. G., & Wyatt, T. J. (2000). A meta-analysis of rates of return to agricultural R and D. IFPRI research report no. 113. International Food Policy Research Institute: Washington, D. C. https://www.ifpri.org/publication/meta-analysis-rates-return-agricultural-r-d
- Blundell, R., & Costa-Dias, M. (2000). Evaluation methods for non-experimental data. Fiscal Studies, 21, 427-468. https://econpapers.repec.org/article/ifsfistud/v_3a21_3ay_3a2000_3ai_3a4_3ap_3a427-468.htm
- Morgan, C. J. (2017). Reducing bias using propensity score matching, Journal of Nuclear Cardiology, 25, 404–406 https://doi.org/10.1007/s12350-017-1012-y
- Chen, S., & Ravallion, M. (2003). Hidden impact? Ex-post evaluation of an anti-poverty program. World Bank Policy Research Working Paper No. 3049. https://doi.org/10.1596/1813-9450-3049
- Dehejia, R. H., & Wahba, S. (2002). Propensity score-matching methods for non-experimental causal studies. The Review of Economics and Statistics, 84, 151-161. https://econpapers.repec.org/article/tprrestat/v_3a84_3ay_3a2002_3ai_3a1_3ap_3a151-161.htm
- Economic Survey, (2019). Directorate of Economics and Statistics, Government of Jammu and Kashmir. http://www.ecostatjk.nic.in/JKINDIANECO/2018.pdf.
- Essama-Nssah, B. (2006). Poverty Reduction Group (PRMPR). The World Bank Washington, D. C. WPS: 3877pp. https://openknowledge.worldbank.org/bitstream/handle/10986/8730/wps38770rev0pdf.pdf.
- Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011). Match It: Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, 42, https://www.jstatsoft.org/article/view/v042i08
- Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica, 62, 467-475. https://www.jstor.org/stable/2951620?seq=1
- Henderson, J., & Chatfield, S. (2011). Who matches? Propensity score and bias in the causal effects of education on participation. The Journal of Politics, 73, 646–658. https://doi.org/10.1017/S0022381611000351
- Jones, N., Jones, H., Steer, L., & Datta, A. (2009). Improving Impact Evaluation, Production and Use. (ODI: Working Paper), 78pp. http://www.odi.org.uk/resources/docs/4158.pdf
- Khalid, S. (2018). Decline in saffron production and its impact on state economy, Kashmir Images, https://thekashmirimages.com/2018/08/30/decline-in-saffron-production-its-impact-on-state-economy/
- Kurth, T., Walker, A. M., Glynn, R. J., Chan, K. A., Gaziano, J. M., & Berger, K. (2006). Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of non-uniform effect. American Journal of Epidemiology, 163, 262–270. https://doi.org/10.1093/aje/kwj047
- Nehvi, F. A., Dhar, J. K., Sheikh, S. S., Iqbal, A. M., & John, A. A. (2018). Conventional postharvest practices and their impact on saffron quality-a study Acta Horticulturae1200. ISHS 2018. https://doi.org/10.17660/ActaHortic.2018.1200.23 Proceeding IV International Symposium on Saffron Biology and Technology, Nehvi, F. A., & Wani, S. A. (Editors).
- Noe´mi, K., Gruber, S., Radice, R., Grieve, R., & Jasjeet, S. S. (2014). Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching, Statistical Methods in Medical Research, 25, 2315-2336 https://doi.org/10.1177%2F0962280214521341
- Rosenbaum, P. R., & Rubin, D. B., (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70, 41-55, https://doi.org/10.1093/biomet/70.1.41.
- Rudner, L. M., & Peyton, J. (2006). Consider propensity score to compare treatments. Practical Assessment, Research and Evaluation, 11, 1-9.https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1195&context=pare
- Sahu, S., K., & Das, S. (2015). Impact of agricultural related technology adoption on poverty: A study of selected households in rural India, Working Paper 131/2015, Madras School of Economics, Gandhi Mandapam Road Chennai 600 025, India, 25pp. http://admin.indiaenvironmentportal.org.in/files/file/Impact%20of%20Agricultural%20Related%20Technology.pdf
- UNIDO. (2014). Saffron Industry Value Chain Development In Iran, Diagnostic Study Report United Nations Industrial Development Organization Vienna International Centre, P.O. Box 300, 1400 Vienna, Austria, https://open.unido.org/api/documents/4672742/download/Saffron%20Industry%20Value%20Chain%20Development%20In%20Iran%20-%20Diagnostic%20Study%20Report
- Westreich, D., Lessler, J., & Funk, M. J. (2010). Propensity score estimation: machine learning and classification methods as alternatives to
- logistic regression. Journal of Clinical Epidemiology, 63, 826-833. https://dx.doi.org/10.1016%2Fj.jclinepi.2009.11.020
References
Adewale A. J., (1993). An assessment of the impact of Farming Systems Research/Extension on the adoption of agricultural technologies in the Middle-Belt region of Nigeria, Retrospective Theses and Dissertations. 10399.
Alston, J. M., Craig, B. J., & Pardey, P. G. (1998). Dynamics in the creation and depreciation of knowledge, and the returns to research. Discuss. Pap. 35, EPTD, International Food Policy Research Institute. https://core.ac.uk/download/pdf/194595032.pdf
Alston J. M., Edwards G. W, Freebairn J. W., (1988). Market distortions and the benefits from research. American Journal of Agricultural Economics, 70, 281-288. https://ideas.repec.org/a/oup/ajagec/v70y1988i2p281-288..html
Alston, J. M., Chan-Kang, C., Marra, M. C., Pardey, P. G., & Wyatt, T. J. (2000). A meta-analysis of rates of return to agricultural R and D. IFPRI research report no. 113. International Food Policy Research Institute: Washington, D. C. https://www.ifpri.org/publication/meta-analysis-rates-return-agricultural-r-d
Blundell, R., & Costa-Dias, M. (2000). Evaluation methods for non-experimental data. Fiscal Studies, 21, 427-468. https://econpapers.repec.org/article/ifsfistud/v_3a21_3ay_3a2000_3ai_3a4_3ap_3a427-468.htm
Morgan, C. J. (2017). Reducing bias using propensity score matching, Journal of Nuclear Cardiology, 25, 404–406 https://doi.org/10.1007/s12350-017-1012-y
Chen, S., & Ravallion, M. (2003). Hidden impact? Ex-post evaluation of an anti-poverty program. World Bank Policy Research Working Paper No. 3049. https://doi.org/10.1596/1813-9450-3049
Dehejia, R. H., & Wahba, S. (2002). Propensity score-matching methods for non-experimental causal studies. The Review of Economics and Statistics, 84, 151-161. https://econpapers.repec.org/article/tprrestat/v_3a84_3ay_3a2002_3ai_3a1_3ap_3a151-161.htm
Economic Survey, (2019). Directorate of Economics and Statistics, Government of Jammu and Kashmir. http://www.ecostatjk.nic.in/JKINDIANECO/2018.pdf.
Essama-Nssah, B. (2006). Poverty Reduction Group (PRMPR). The World Bank Washington, D. C. WPS: 3877pp. https://openknowledge.worldbank.org/bitstream/handle/10986/8730/wps38770rev0pdf.pdf.
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011). Match It: Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, 42, https://www.jstatsoft.org/article/view/v042i08
Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica, 62, 467-475. https://www.jstor.org/stable/2951620?seq=1
Henderson, J., & Chatfield, S. (2011). Who matches? Propensity score and bias in the causal effects of education on participation. The Journal of Politics, 73, 646–658. https://doi.org/10.1017/S0022381611000351
Jones, N., Jones, H., Steer, L., & Datta, A. (2009). Improving Impact Evaluation, Production and Use. (ODI: Working Paper), 78pp. http://www.odi.org.uk/resources/docs/4158.pdf
Khalid, S. (2018). Decline in saffron production and its impact on state economy, Kashmir Images, https://thekashmirimages.com/2018/08/30/decline-in-saffron-production-its-impact-on-state-economy/
Kurth, T., Walker, A. M., Glynn, R. J., Chan, K. A., Gaziano, J. M., & Berger, K. (2006). Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of non-uniform effect. American Journal of Epidemiology, 163, 262–270. https://doi.org/10.1093/aje/kwj047
Nehvi, F. A., Dhar, J. K., Sheikh, S. S., Iqbal, A. M., & John, A. A. (2018). Conventional postharvest practices and their impact on saffron quality-a study Acta Horticulturae1200. ISHS 2018. https://doi.org/10.17660/ActaHortic.2018.1200.23 Proceeding IV International Symposium on Saffron Biology and Technology, Nehvi, F. A., & Wani, S. A. (Editors).
Noe´mi, K., Gruber, S., Radice, R., Grieve, R., & Jasjeet, S. S. (2014). Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching, Statistical Methods in Medical Research, 25, 2315-2336 https://doi.org/10.1177%2F0962280214521341
Rosenbaum, P. R., & Rubin, D. B., (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70, 41-55, https://doi.org/10.1093/biomet/70.1.41.
Rudner, L. M., & Peyton, J. (2006). Consider propensity score to compare treatments. Practical Assessment, Research and Evaluation, 11, 1-9.https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1195&context=pare
Sahu, S., K., & Das, S. (2015). Impact of agricultural related technology adoption on poverty: A study of selected households in rural India, Working Paper 131/2015, Madras School of Economics, Gandhi Mandapam Road Chennai 600 025, India, 25pp. http://admin.indiaenvironmentportal.org.in/files/file/Impact%20of%20Agricultural%20Related%20Technology.pdf
UNIDO. (2014). Saffron Industry Value Chain Development In Iran, Diagnostic Study Report United Nations Industrial Development Organization Vienna International Centre, P.O. Box 300, 1400 Vienna, Austria, https://open.unido.org/api/documents/4672742/download/Saffron%20Industry%20Value%20Chain%20Development%20In%20Iran%20-%20Diagnostic%20Study%20Report
Westreich, D., Lessler, J., & Funk, M. J. (2010). Propensity score estimation: machine learning and classification methods as alternatives to
logistic regression. Journal of Clinical Epidemiology, 63, 826-833. https://dx.doi.org/10.1016%2Fj.jclinepi.2009.11.020