Pages 2021-3.cdr INTRODUCTION The ATMA model, a decentralised, market driven extension model, was introduced under the Na�onal Agricultural Technology Project (NATP) as a solu�on to the challenges faced by Training and Visit System which was plagued by unrelen�ng fund requirements and inadequate quality- employees. (Anderson and Feder, 2004; Reddy and Swanson, 2006;Swanson et al., 2008; Babu et al., 2013). "Support to State Extension Programs for Extension Reforms" widely known as Agriculture Technology Management Agency (ATMA) Scheme was first implemented in 2005 and presently is func�oning in 691 districts of 28 states and 5 Union Territories throughout India. Since i t s i n c e p � o n , t h e AT M A h a s b e e n dissemina�ng extension services to the agriculture and allied sectors of the country i n t h e f o r m o f Fa r m e r s Tr a i n i n g , Demonstra�ons, Exposure Visits, Kisan Mela, Mobiliza�on of Farmers Groups and organizing Farm Schools at the district level. In the year 2021, 1370654 farmers benefi�ed from na�onwide provision of 6659 Research Article Journal of Extension Educa�on Vol. 33 No.3, 2021 DOI: h�ps://doi.org/10.26725/ 1 3 3 659 6670JEE.202 . .3 .6 - Impact of Extension Services Provided by ATMA (Agricultural Technology Management Agency) on Small and Marginal Farmers in Rural Assam Christopher Tirkey and Manesh Choubey ABSTRACT Agricultural Extension Services aim at dissemina�ng new knowledge and skill to farmers to aid them in adop�ng new agricultural technologies and use their resources efficiently. Agricultural knowledge improves their skill and decision-making and enhances more efficient u�liza�on of agricultural technologies. With a sample of 160 famers collected from Golaghat district of Assam by using mul�-staged random sampling method, this study a�empts to understand the impact of extension services provided by ATMA (Agricultural Technology Management Agency) in rural Assam. The Propensity Score Matching (PSM) technique is employed to control for poten�al sample selec�on biases. The analysis and findings reveal that the extension services provided by ATMA in the study area posi�vely impacts on the income and paddy produc�on of the small and marginal farmers. Timely dissemina�on of extension services which meet the actual needs of the farmers can impact the farmers income and output produc�on to larger extent. Keywords: ATMA; Agricultural Extension Services; Propensity Score Matching; Small and marginal farmers; Assam Dept of Economics, Sikkim University, Gangtok, Sikkim - 737 102 Received : 10-02-2022 Accepted: 05-09-2022 extension services by ATMA. Of the total b e n e fi c i a r i e s a b o u t 4 5 p e rc e n t h a d par�cipated in training programs and 13 percent in demonstra�ons organized by ATMA. In Assam, the number of par�cipants in trainings programmes has shown an increasing trend since a decade and in 2021, it shared about 91 percent of the total b e n e fi c i a r i e s o f e x t e n s i o n s e r v i c e s provided by ATMA in the state (Ministry of Agriculture and Farmers Welfare). Interna�onal studies have generally e v a l u a t e d e x t e n s i o n s y s t e m a n d methodology and have found mixed results (Dercon et al., 2009 ; Davis et al., 2012; Hunt et al., 2014; Läpple and Hennessy 2015; Josephat and Rose, 2015 Cawley et al., 2018; Teka and Lee, 2019). Previous researches have also been conducted to see the impact of extension services of ATMA in India and these studies too have found mixed results (IIM, Lucknow, 2004a; 2004b; Swanson et al., 2009; Singh, et al., 2014; Babuet al. 2013;Saikia et al., 2013; Biam and Barman, 2017; Goswami and Bezbaruah, 2017; Walling et al. 2017; Deka et al. 2017; Bortamuly and Das, 2018; Shita et al., 2020). Most of these studies have focused on the implementa�on and ins�tu�onal achievements ATMA, on the organiza�onal performance of the agency and on the nature and effec�veness of adop�on of technology, and therefore, there are limited systema�c farm-level studies which have looked into the impact of extension services provided by ATMA on total output produc�on and income of the farmers. This calls for assessing the impact of extension services on the total output produc�on and income of the farmers. A review of previous studies on the impact of extension interven�ons by Anderson and Feder (2004) warns that the mixed results obtained in the previous studies should be treated with cau�on b e c a u s e o f e x i s t e n t e c o n o m e t r i c c h a l l e n ge s . A s s e s s i n g t h e i m p a c t of extension services is, indeed, a challenge (Ragasa et al. 2016) because of the vast range and diversity in the methods of extension and the outcome measures which might lead to possible inconsistent results (Läpple and Hennessy, 2015). However, viewing from the policy perspec�ves, the ul�mate criterion is to understand the impact of these extension interven�ons (Birneret al., 2009). Heinrich et al.,(2010) and Duflo and Kremer (2003) point towards p r o b l e m s n a m e l y, e s t a b l i s h i n g t h e counterfactual; an adequate group for c o m p a r i s o n ; s a m p l e s e l e c � o n b i a s . U n fo r t u n a t e l y, m o s t o f t h e s t u d i e s co n ce r n i n g t h e i m p a c t of ex te n s i o n interven�ons in the past have been assessed by looking at the pre-interven�on and post interven�on observa�ons with li�le considera�on to the counterfactual factors (Josephat and Likengaga, 2015). Accordingly, the purpose of this study is to see the impact of the extension service provided by ATMA on farmers output produc�on and farm-level income a�er controlling for poten�al sample selec�on biases.Our study a�empts to understand the impact of extension 6660 Journal of Extension Educa�on services by using the Propensity Score Matching (PSM), which addresses the fundamental problems associated with impact evalua�on, and also controls for possible sample selec�on bias. METHODOLOGY The present study was conducted in Golaghat district of Assam which comprises about 2.03 lakh farm families, who are engaged in paddy produc�on. Primary data for the study were collected by conduc�ng a field survey in which the head of the farmer household was interviewed. It is to be men�oned here that, being the main crop produced in the district, paddy crop focused in the study. The universe of the study being vast and the researcher facing resource and �me constraints, four blocks in Golaghat district were selected for field survey given their level of paddy produc�on. For the selec�on of farm households, in the present study, a mul�-staged random sampling method was used. Ini�ally, four blocks in the district, namely- Kathalguri, Kakodonga, Gomariguri and Morangi, were selected for the present study. From each block, four Gram Panchayat Units (GPUs) were selected randomly. From each GPU, one village was selected randomly and finally, from each v i l l a g e , t e n f a r m - h o u s e h o l d s w e r e interviewed randomly. Thus, the total sample included one hundred and sixty farmers, of which fi�y percent farmers were beneficiaries of ATMA, and had a�ended t r a i n i n g p r o g r a m m e s a n d m e t h o d demonstra�on in line plan�ng, nutrient management and its applica�on and spraying of insec�cides in 2019 and 2020. The remaining had never received extension service in any form from ATMA. Primary Data was collected by interviewing the head of the farmer household using an interview schedule which was prepared by consul�ng the exis�ng literature. Data on various aspects of agriculture like land holding, the socio-economic profile of the farmer household, access to extension services provided by ATMA and the quality and usefulness of the technology disseminated a t t h e d i s t r i c t l eve l by A g r i c u l t u r a l Technology Management Agency were recorded with the help the interview schedule during December, 2020 and January, 2021 through field survey. 6661Impact of Extension Services Provided by ATMA (Agricultural Technology Management Agency) on Small and Marginal Farmers in Rural Assam To u n d e r s t a n d t h e i m p a c t o f extension services provided by ATMA in the study area the Propensity Score Matching (PSM) technique, introduced by Rosenbaum and Rubin (1983) was employed. Propensity Score Matching refers to the pairing of treatment and controlled observa�ons having similar values on their propensity scores for an individual (i) as the condi�onal probability (p) of receiving a par�cular treatment given a vector of observed covariates (Z) and is expressed as: where, D indicates the exposure to treatment. It takes the value 1 for receiving treatment or membership in the treated group and 0 for not receiving treatment or m e m b e r s h i p i n co n t ro l l e d g ro u p . Z i r e p r e s e n t s t h e v e c t o r o f o b s e r v e d t h covariates for the i individual. The exposure to treatment within the cells defined by the values of the mono- dimensional variables p(Z) is random if the exposure to treatment wihin the cells defined by Zis random.p(Z) is also known as the Average effect of Treatment on the Treated (ATET) is a prominent es�mator as it explicitly focuses on the effects on those for whom the scheme is intended, and is expressed as 6662 Figure 1: Map of the study area Journal of Extension Educa�on Where, the outer expecta�on is over the distribu�on of (p(Z )}|D i=1) andy and y are i 1i 0i the possible outcomes of the treatment and non-treatment respec�vely. The expected outcome of the average treatment effect for the treated is the difference between the outcomes of the treated and of the treatment, had they not been treated. One of the major problems in es�ma�ng treatment effects is the selec�on biases that arise because of the differences between the treated and non-treated groups for reasons other than treatment status. The Propensity Score Matching technique is usually used in evalua�on studies to correct for poten�al bias arising in the data due to differences between the treatment and controlled observa�ons (Godtland et al.,2004; Mendola, 2007; Ali and Rahut, 2013; Teka and Lee, 2019; Shita et al., 2020). FINDINGS AND DISCUSSION General Characteris�cs of the Sampled Farmers T h e s a m p l e d f a r m e r s ' socioeconomic profile helps to understand t h e c h a r a c t e r i s � c s o f t h e f a r m e r s ' households in the study area. Table1 provides informa�on on the general characteris�cs of the sampled farmers which helps to iden�fy the broad socio- economic characteris�cs of both the groups of farmers in the study area. Efforts have been made to understand the level of living of the farmers through the sampled farmers' age and experience in agriculture and allied ac�vi�es, years of schooling, opera�onal land holding, produc�on and annual income.It is evident from Table 1 that, on average, most of the farmers are adults have considerable years of experience in paddy farming. The average years of schooling of the sampled farmers is about ten years which implies that farmers in the study area have received high school educa�on. The average size land-holding of the total sampled farmers as evident from the table indicates that most of the farmers are small and marginal land holders. The average family size of the sampled farmer household is about 5 members. It is also seen that on average the beneficiary farmers produce about 74 quintals of paddy and their average annual income is about INR 129000.The non-beneficiary farmers, on the other hand, produce on an average of about 48 quintals and their average annual income is about INR 83995.The perusal of Table 1 reveals that there is significant mean difference in produc�on and income between the beneficiaries of ATMA and the non- beneficiary farmers who have not received any benefits from ATMA. A sta�s�cally significant difference in the produc�on between the two categories of farmers, with a mean produc�on difference of about 25 kilograms, is seen in the table. The observa�on is similar between the two groups of farmers in terms of Income. 6663Impact of Extension Services Provided by ATMA (Agricultural Technology Management Agency) on Small and Marginal Farmers in Rural Assam 1 Age 43.52 (7.78) 43.78 (10.83) -.5375 -0.3603 (1.491) 2 Educa�on 10.16 (3.83) 9.52 (3.23) .6375 1.1370 (.560) 3 Family size 4.85 (1.09) 4.68 (1.22) .1625 0.8844 (0.183) 4 Land-Holding 1.34 (0.77) 0.84 (0.38) .5003 1.4578 (0.146) 5 Produc�on 73.62 (44.21) 48.33 (26.41) 25.29 (5.763) 6 Income 129000 (70277.08) 83995 (42972.73) 45272.5 4.9157*** (9209.725) Sl.No. Variable Mean Beneficiaries Non-Beneficiaries Mean Difference t (SE) A sta�s�cally significant difference in the income between the two categories of farmers, with a mean income difference of INR.45272, is no�ced from the table. Howeve r, n o s t a � s � c a l l y s i g n i fi c a nt differences are no�ced in the other variables between the farmers who have received extension services from ATMA and the farmers who have not received any agricultural extension benefits. Therefore, it can be said that there is significant evidence that to support the fact that extension services provided by ATMA impact the farmers' produc�on and income. Treatment Effect The Probit model, with extension beneficiary as the dependent variable and other demographic and socioeconomic variables as explanatory variables, is used to es�mate the propensity scores. All the e s � m a � o n s w e r e d o n e u s i n g t h e 6664 Table 1. General Characteris�cs of the Sampled Farmers Note: *** indicate that the results are sta�s�cally significant at 1 percent level of significance "pscore.ado" module in the STATA so�ware. The result of the Probit Regression, based on which the propensity scores were es�mated, is presented in Table 2. The dichotomous variable extension beneficiary was treated as the dependent variable that assumed a value of "1" if the farmer household was a beneficiary and "0" if not. The explanatory variable included the farmer's age, the farmer's experience in paddy farming, size of land-holding of the farmers, and the farmer's income. The 2 probability of the LR X sta�s�c is 0.000, indica�ng that the es�mated probit regression is significant at a 1 percent level. Ta b l e 2 s h o w s t h a t t h e f a r m e r s ' par�cipa�on in the extension services is significantly influenced by age, experience, land-holding and income. The variable age has a nega�ve sign indica�ng that younger farmers have a greater probability of Journal of Extension Educa�on receiving extension services and the probability of par�cipa�on in extension services decreases as the farmers get older. Similar finding was recorded by Suvediet al. (2017). This implies that the younger farmers are the main beneficiaries of the extension services provided by ATMA. It could be due to the risk bearing nature of the young farmers than the older farmers. 6665 Table 2. Results of Probit es�ma�on of Propensity Scores Explanatory Variables Coefficients P value Age - 0.105 (0.022) 0.000 Educa�on 0.037 (0.034) 0.255 Experience 0.111 (0.019) 0.000 Family size - 0.040 (0.105) 0.703 Land holding 0.860 (0.336) 0.000 Off Farm Income 0.000 (3.120) 0.010 Constant 1.060 (0.854) 0.214 Number of Observa�on 160 LR X2 (6) 69.41 P > X2 0.000 Pseudo R 2 0.312 The coefficient of experience is posi�ve and significant indica�ng that farmers with more years of experience in paddy farming had greater probability of receiving extension services delivered by ATMA. Ainembabazi and Mugisha (2014), h o w e v e r, s u g g e s t t h a t e x p e r i e n c e determines the farmers' a�tude and decision towards adop�on, reten�on and rejec�on of a technology. The coefficient of land is posi�ve and significant indica�ng that land-ownership as an important factor for receiving extension services. Similarly, farmers with higher income had greater probability of receiving extension services. The farmers with higher income also have the ability to purchase new technology and bear its deprecia�on cost. To proceed with the es�ma�on of the Average Treatment Effe c t o n t h e Tre at e d ( AT T ) , a l l t h e assump�ons of propensity score matching have been achieved and the region of the "common support" is 0.005 and 0.999. Table 3 presents the descrip�on of the es�mated propensity scores in the region of common support. Impact of Extension Services Provided by ATMA (Agricultural Technology Management Agency) on Small and Marginal Farmers in Rural Assam 6666 The mean value and the standard devia�on of the es�mated propensity score within this region of common support are 0 . 5 1 3 a n d 0 . 2 9 0 re s p e c � v e l y. T h e balancing property was sa�sfied and the e s � m a t e d p r o p e n s i t y s c o r e s a r e categorised into five blocks which ensured that the mean propensity score of the treated and control group in each block is not different and it facilitates matching to be done with minimum bias. The propensity score matching results for the Average Treatment Effect on the Treated (ATT) are p re s e nte d i n t h e Ta b l e 4 . Di ffe re nt m a t c h i n g a l g o r i t h m s l i k e N e a r e s t Neighbour Matching (NNM), Radius Matching (RM), Kernal Matching (KM) and S t r a � fi c a � o n M a t c h i n g ( S M ) w e r e employed for the analysis. The outcome variable is the total paddy produc�on. Table 3. Es�mated Propensity Score in the Region of Common Support Percentage Percen�les Smallest 0.0054 0.0088 0.0088 0.0119 0.9829 0.9871 0.9952 1 % 5% 10% 25% 50% 75% 90% 95% 99% 0.0088 0.0303 0.0967 0.2647 0.5384 0.7745 0.9107 0.9574 0.9952 0.9989 Number of Observa�on 158 Mean 0.5131 Standard Devia�on 0.2908 Variance 0.0845 Table 4. Effect of Extension Services Provided by ATMA on Paddy Output: Matching Es�mates Matching Algorithm Outcome Variable ATT Standard Error Number of Treated Number of Observed NNM Paddy produc�on 2.075 5.401 80 26 KM Paddy produc�on 4.349 5.678 80 78 RM Paddy produc�on 5.385 2.815 62 77 SM Paddy produc�on 0.466 8.625 80 78 Journal of Extension Educa�on From the above discussion, it is seen that the total produc�on of the beneficiary farmers is more than the non-beneficiaries. The ATT results from the different matching methods indicate that the difference of the total produc�on of the beneficiaries and the non-beneficiaries range between 0.47 quintals to 5.38 quintals. Similar findings have been documented by Hasan et al (2013) that access to extension services raised the value of crop produc�on per hectare by 14.4 %. Several studies highlight that contact with extension services raises total output (Birkhaeuser, et al, 1991). Ali and Rahut (2013) and Teka and Lee (2019) found that beneficiary farmers obtained higher crop yields. CONCLUSION In this study, it is found that a�er sharing similar characteris�cs, farmers who were beneficiaries of ATMA had total produc�on higher than the farmers who had never received extension benefits in any form. Differences in the average produc�on of the beneficiary farmers and the non- beneficiary farmers have been found in the study, with the average produc�on of the beneficiary farmers being more than that of the non-beneficiary farmers. This difference in the total produc�on of paddy between the two groups of farmers can be credited to the u�liza�on of the agricultural knowledge which the beneficiary farmers had received in the form of trainings programmes and method demonstra�on, provided by ATMA. The treatment effect analysis employed in the study revealed that the extension services provided by ATMA in the Golaghat district of Assam posi�vely impact the income and produc�on of the farmers. Since the majority of the farmers in the district comprise small and marginal farmers, t h e r e f o r e , t h e e x t e n s i o n a c � v i � e s undertaken by ATMA are projected mostly towards these farmers and towards paddy cul�va�on which is the main crop cul�vated in the district. Timely dissemina�on of extension services, which meet the actual needs of the farmers, can affect the farmers income and output produc�on to larger extent. REFERENCES Ainembabazi, J. H., & Mugisha, J. (2014). The Role of Farming Experience on t h e A d o p � o n o f A g r i c u l t u r a l Te c h n o l o g i e s : E v i d e n c e f r o m Smallholder Farmers in Uganda. Journal of Development Studies, 50 (5), 666-679. Ali, A., &Rahut, D. B. (2013). Impact of Agricultural Extension Services on Technology Adop�on and Crops Yield: Empirical Evidence from Pakistan. Asian Journal of Agriculture and Rural Development, 11 (3), 801- 812. An d e rs o n , R. J. , & Fe d e r, G. ( 2 0 0 4 ) . A g r i c u l t u r a l E x t e n s i o n : G o o d Intens�on and Hard Reali�es. The World Bank Research Observer, 19 (1), 41-60. 6667Impact of Extension Services Provided by ATMA (Agricultural Technology Management Agency) on Small and Marginal Farmers in Rural Assam Babu, S.C., Joshi, P.K., Glendenning, C.J., A s e n s o - O k y e r e , K w a d w o . , &Sulaiman V., R. (2013). The State of Agricultural Extension Reform in India: Strategic Priori�es and Policy Op�ons. A g r i c u l t u re Eco n o m i c s Research Review, 26 (2), 159-172. B i a m , K . P. , & B a r m a n , U . ( 2 0 1 7 ) . E ff e c � v e n e s s o f R e s e a r c h - E x t e n s i o n - Fa r m e r l i n k a g e s o f A g r i c u l t u r a l T e c h n o l o g y Management Agencies in Assam, India. Interna�onal Journal of Current Microbiology and Applied Sciences, 6(12), 1873–1883. Birner, R., Davis, K., Pender, J., Nkonya, E., Anandajayasekeram, P., Ekboir, J., Mbabu, A., Spielman, D.J., Horna, D., Benin, S., & Cohen, M. (2009). From best prac�ce to best fit: A framework f o r d e s i g n i n g a n d a n a l y z i n g pluralis�c agricultural advisory services worldwide. Jo u r n a l of agricultural educa�on and extension, 15(4), 341-355. B o r t a m u l y , D . , & D a s , P. ( 2 0 1 8 ) . Performance of different role items as perceived by the agricultural e x t e n s i o n p e r s o n n e l i n t h e revitalized extension system in Assam. Agriculture Update, 13(2), 211–216. Cawley, A., O'Donoghue, C., Heanue, K., Hilliard, R., & Sheehan, M. (2018). The impact of extension services on farm-level income: An instrumental v a r i a b l e a p p r o a c h t o c o m b a t endogeneity concerns. Applied Economic Perspec�ves and Policy, 40(4), 585-612. Davis, K., Nkonya, E., Kato, E., Mekonnen, D. A., Odendo, M., Miiro, R., &Nkuba, J. (2012). Impact of farmer field schools on agricultural produc�vity and poverty in East Africa. World development, 40(2), 402-413. Deka, C., Mishra, P., & Baruah, R. (2017). Organiza�onal Level Performance of A g r i c u l t u r a l T e c h n o l o g y Management Agency (ATMA) under New Extension Reforms in the State of Assam. Asian Journal of Agricultural Extension, Economics & Sociology, 19(2), 1–7. Dercon, S., Gilligan, D. O., Hoddino�, J., &Woldehanna, T. (2009). The impact of agricultural extension and roads on poverty and consump�on growth i n fi � e e n E t h i o p i a n v i l l a g e s . American Journal of Agricultural Economics, 91(4), 1007-1021. Duflo, E., & Kremer, M. (2003, July). Use of randomiza�on in the evalua�on of development effec�veness. In World B a n k O p e r a � o n s E v a l u a � o n Department (OED) Conference on E v a l u a � o n a n d D e v e l o p m e n t Effec�veness (Vol. 15). Godtland, E. M., Sadoulet, E., Janvry, A. d., Murgai, R., & Or�z, O. (2004). The 6668 Journal of Extension Educa�on Impact of Farmer Field Schools on Knowledge and Produc�vity: A Study of Potato Farmers in the P e r u v i a n A n d e s . E c o n o m i c Development and Cultural Change, 53, 63-92. Goswami, B., &Bezbaruah, R. J. (2017). Co m p a r i n g p u b l i c a n d p r i va t e agricultural extension services- insights from the Brahmaputra Valley of Assam. Economic and Poli�cal Weekly, 52(52). Hasan, M. F., IMAI, K. S., & SATO, T. (2013). Impact o Agricultural Extension on C r o p P r o d u c � v i t y, Po v e r t y a n d Vulnerability: Evidence from Uganda. Heinrich, C., Maffioli, A., & Vazquez, G. ( 2 0 1 0 ) . A p r i m e r fo r a p p l y i n g propensity-score matching. Inter- American Development Bank. Hunt, W., Birch, C., Vanclay, F., & Cou�s, J. (2014). Recommenda�ons arising from an analysis of changes to the Australian agricultural research, development and extension system. Food Policy, 44, 129-141. Indian Ins�tute of Management, Lucknow. (2004a). Successful Case Studies, Interven�ons and Innova�ons in Te c h n o l o g y D i s s e m i n a � o n , Agriculture Management Centre. Indian Ins�tute of Management, Lucknow.( 2004b). Impact Assessment Report, on the Innova�ons in Technology Dissemina�on (ITD) Component of the Na�onal Agricultural Technology Project, Agriculture Management Centre. Josephat, P., &Likangaga, R. (2015). Analysis of Effects of Agriculture Interven�on Using Propensity Score Matching. Journal of Agricultural Studies, 3(2), 49. Läpple, D., & Hennessy, T. (2015). Assessing the impact of financial incen�ves in extension programmes: evidence from Ireland. Journal of Agricultural Economics, 66(3), 781-795. M e n d o l a , M . ( 2 0 0 7 ) . A g r i c u l t u r a l Technology Adop�on and Poverty Reduc�on: A Propensity-Score M a t c h i n g A n a l y s i s f o r R u r a l Bangladesh. Food Policy, 32 (3), 372–393. R a g a s a , C . , U l i m w e n g u , J . , Randriamamonjy, J., &Badibanga, T. ( 2 0 1 6 ) . F a c t o r s a ff e c � n g p e r f o r m a n c e o f a g r i c u l t u r a l e x t e n s i o n : E v i d e n c e f r o m Democra�c Republic of Congo. The Journal of Agricultural Educa�on and Extension, 22(2), 113-143. Reddy, M., & Swanson, B.E. (2006). Starategy for Upscaling the ATMA Model in India. Proceedings of the 2 2 n d A n n u a l M e e � n g s o f t h e A s s o c i a � o n f o r I n t e r n a � o n a l Agricultural and Extension Educa�on , Clearwater Beach, FL, 14–17 May, 6669Impact of Extension Services Provided by ATMA (Agricultural Technology Management Agency) on Small and Marginal Farmers in Rural Assam 2006. Rosenbaum, P., & Rubin, D. (1983). A s s e s s i n g s e n s i � v i t y t o a n unobserved binary covariate in an observa�onal study with binary o u t c o m e . J o u r n a l o f t h e Ro y a l Sta�s�cal Society, Series B, 45, 212- 218. Saikia, P., Das, M. D., & Deka, M. B. (2018). Impact of agricultural extension services on empowerment of farm women of Assam. Asian Journal of Home Science, 13(1), 37–46. Shita, A., Kumar, N., & Singha, S. (2020). Produc�vity and welfare effects of agricultural technologies: A study of maize producing households in Ethiopia using PSM approach. Indian Journal of Engineering & Materials Sciences, 27, 921-926. Singh, K. M., Meena, M. S., Swanson, B. E., Reddy, M. N., &Bahal, R. (2014). In- Depth Study of the Pluralis�c Agricultural Extension System in India. Suvedi, M., Ghimire, R., & Michael, K. (2017). Farmers' par�cipa�on in extension programs and technology adop�on in rural Nepal: a logis�c regression analysis. The Journal of Agricultural Educa�on and Extension, 23 (4), 351-371. Swanson, B., Singh, K. M., & Reddy, M. N. ( 2 0 0 8 ) . A d e c e n t r a l i z e d , p a r � c i p a t o r y , m a r k e t - d r i v e n extension system: The ATMA model in India. Par�cipatory, Market-Driven Extension System: The ATMA Model in India (October 10, 2008). Teka, A. M., & Lee, S.-K. (2019). The impact of agricultural package programs on farm produc�vity in Tigray-Ethiopia: Pa n e l d at a e s � m a� o n . Co g e n t Economics & Finance, 7:1631987. Walling, I., Amod, S., Yadav, M. K., Rajbhar, A. K., &Kankaba�, K. (2017). Impact o f a g r i c u l t u r a l t e c h n o l o g y m a n a g e m e n t a g e n c y o n r u r a l economy of Nagaland, India. Plant Archives, 17(2), 1511-1516. 6670 Journal of Extension Educa�on Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Page 8 Page 9 Page 10 Page 11 Page 12