Technology adoption has been advocated to be an important way to improve agricultural productivity and welfare of the farmers in the semi-arid regions across the globe. Government of Karnataka implemented Bhoosamrudhi programme in four districts of the state (Bidar, Chikballapur, Dharwad and Udupi) as a pilot project to increase the crop yield and income of smallholder farmers. This programme was launched on the theme of technology adoption, along with convergence among different departments of agriculture. Farmers have been classified into five categories based on their levels of technology intensification to assess the impact of different levels of technology intensification on their income level. The research is built on a primary survey conducted in pilot districts of the state in 2018 over a sample of 1,465 farmer households. The results are generated using econometric methods of propensity score matching (PSM) and inverse probability weighted with regression adjustment (IPWRA), which highlight that higher the intensification, higher the net returns to the farmers. The results state that non-adopters would benefit additional ₹3,200 per month if they adopt at least one level of technology intensification. Hence, this programme turned out to be a successful model for smallholder farmers in semi-arid region of India. Steps should be taken to maintain and expand the momentum of the adoption to ensure food and livelihood security in the economy.
AdolwaI. S., SchwarzeS., & BuerkertA. (2019). Impacts of integrated soil fertility management on yield and household income: The case of Tamale (Ghana) and Kakamega (Kenya). Ecological Economics, 161, 186–192.
2.
AleneA. D., & CoulibalyO. (2009). The impact of agricultural research on productivity and poverty in sub-Saharan Africa. Food Policy, 34(2), 198–209.
3.
AlwangJ., GotorE., ThieleG., HareauG., JaletaM., & ChamberlinJ. (2019). Pathways from research on improved staple crop germplasm to poverty reduction for small holder farmers. Agricultural Systems, 172, 16–27.
4.
AsfawS., BattistaF. D., & LipperL. (2016). Agricultural technology adoption under climate change in the Sahel: Micro evidence from Niger. Journal of African Economics, 25(5), 637–669.
5.
AsfawS., ShiferawB., SimtoweF., & LipperL. (2012). Impact of mordern agricultural technologies on smallholder welfare: Evidence from Tanzania and Ethiopia. Food Policy, 37(3), 283–95.
6.
BalaineL., DillonE. J., LappleD., & LynchJ. (2020). Can technology help achieve sustainable intensification? Evidence from milk recording on Irish dairy farms. Land Use Policy, 92, 1–12.
7.
BeckerO. S., & IchinoA. (2002). Estimation of average treatment effects based on propensity score. Stata Journal, 2(4), 358–377.
8.
BelowT., ArtnerA., & SieberS. (2010). Micro-level practices to adapt to climate change for African and small-scale farmers: A review of selected literature. International Food Policy Research Institute (IFPRI).
9.
BezuS., KassieG. T., ShiferawB., & Ricker-GilbertJ. (2014). Impact of improved maize adoption on welfare of farm households in Malawi: A panel data analysis. World Development, 59, 120–131.
10.
ChavanP., & RamakumarR. (2002). Micro-credit and rural poverty: An analysis of empirical evidence. Economic & Political Weekly, 37(10), 955–965.
11.
CurtisL. H., HammilB. G., EisensteinE. L., KramerJ. M., & AnstromK. J. (2007). Using inverse probability-weighted estimators in comparitive effectiveness analyses with observational databases. Medical Care, 45(10), 103–107.
12.
DeGraft-JohnsonM., SuzukiA., SakuraiT., & OtsukaK. (2014). On the transferability of the Asian rice green revolution to rainfed areas in sub-Saharan Africa: An assessment of technology intervention in Northern Ghana. Agricultural Economics, 45(5), 555–570.
13.
DrukkerD. M. (2016). Estimating treatment effects from observational data using teffects, stteffects, and eteffects. UK Stata User Group Meeting.
14.
FafchampsM., IslamA., MalekA., & PakrashiD. (2021). Mobilizing P2P diffusion for new agricultural practices: Experimental evidence from Bangladesh. The World Bank Economic Review, 35(4), 1076–1101.
15.
FafchampsM., IslamA., MalekM. A., & PakrashiD. (2020). Can referral improve targeting? Evidence from an agricultural training experiment. Journal of Development Economics, 144, 102436.
16.
HiranoK., & ImbensG. W. (2001). Estimation of causal effects using propensity score matching: An application to data on right heart catheterization. Health Services and Outcome Research Methodology, 2(3–4), 259–278.
17.
HollandP. W. (1986). Statistics and causal inference. Journal of American Statistical Association, 81(396), 945–960.
18.
HossainM., MalekM. A., HossainM. A., RezaM. H., & AhmedM. S. (2019). Agricultural microcredit for tenant farmers: Evidence from a field experiment in Bangladesh. American Journal of Agricultural Economics, 101(3), 692–709.
19.
HuberC. (2015). Introduction to treatment effects in Stata: Part 1. The Stata Blog.
20.
ImbensG. W., & WooldridgeJ. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47, 5–86.
21.
JayneT. S., MasonN. M., BurkeW. J., & ArigaJ. (2018). Review: Taking stock of Africa’s second generation agricultural input subsidy programs. Food Policy, 75, 1–14.
22.
JayneT. S., SnappS., PlaceF., & SitkoN. (2019). Sustainable agricultural intensification in an era of rural transformation in Africa. Global Food Security, 20, 105–113.
23.
JumbriI. A., NasirN. N., SharifS. M., ZainudinM. Z., & IsmailA. F. (2020). Microcredit, technology adoption and economic development of rural households. International Journal of Human Technology & Interaction, 4(1), 59–69.
24.
KassieM., MarenyaP., TessemaY., JaletaM., ZengD., ErensteinO., & RahutD. (2018). Measuring farm and market level economic impacts of improved maize production technologies in Ethiopia: Evidence from panel data. Journal of Agricultural Economics, 69(1), 76–95.
25.
KhonjeM., MandaJ., AleneA. D., & KassieM. (2015). Analysis of adoption and impacts of improved maize varieties in easter Zambia. World Development, 66, 695–706.
26.
KiresurV. R., NayakM. R., GaddiG. M., & KhyadagiK. S. (2017). Improved farm technology adoption and its role in doubling farmers’ income: A case of dry zones in Karnataka. Agricultural Economics Research Review, 30, 217–231.
27.
LybbertT., & SummerD. (2010). Agriculture technologies for climate change mitigation and adaptation in developing countries: Policy options for innovation and technology diffusion. International Center for Trade and Sustainable Development (ICTSD) and International Food and Agriculture Trade Policy Council (IPC).
28.
MandaJ., AleneA. D., TufaA. H., AbdoulayeT., WossenT., ChikoyeD., & ManyongV. (2019). The poverty impacts of improved cowpea varieties in Nigeria: A counterfactual analysis. World Development, 122, 261–271.
29.
MandaJ., GardebroekC., KuntashulaE., & AleneA. D. (2018). Impact of improved maize varieties on food security in eastern Zambia: A double robust analysis. Review of Development Economics, 22(4), 1709–1728.
30.
MariyonoJ., Gunistiyo WaskitoJ., Sumarno NurwildaniM. F., & KuntariningsihA. (2019). Role of microcredit and technology adoption in sustaining farmers’ welfare. Journal of Rural Development, 38(1), 102–119.
31.
MendolaM. (2007). Agricultural technology adoption & poverty reduction: A propensity score matching analysis for rural Bangladesh. Food Policy, 32, 372–393.
32.
PanY., SmithS., & SulaimanM. (2018). Agricultural extension and technology adoption for food security: Evidence from Uganda. American Journal of Agricultural Economics, 100(4), 1012–1031.
33.
PingaliP. L. (2012). Green Revolution: Impacts, limits and the path ahead. Proceedings of the National Academy of Sciences, 109(31), 12302–12308.
34.
PingaliP., AiyarA., AbrahamM., & RahmanA. (2019). Agricultural technology for increasing competitiveness of small holders. In PingaliP., AiyarA., AbrahamM. & RahmanA.Transforming food systems for a rising India. Palgrave.
35.
RosenbaumP. R., & RubinD. B. (1983). The central role of propensity score in observational studies for causal effects. Biometrika, 70, 41–55.
36.
RosenbaumP. R., & RubinD. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 29(1), 33–38.
37.
RubinD. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592.
38.
RubinD. B. (1997). Estimating causal effects from large data sets using porpensity scores. Annals of Internal Medicine, 127, 757–763.
39.
SahuS. K., & DasS. (2015). Impact of agricultural related technology adoption on poverty: A study of select households in India(Working Paper 131/2015). Madras School of Economics.
40.
SinyoloS. (2020). Technology adoption and household food security among rural households in South Africa: The role of improved maize varieties. Technology in Society, 60, 101214.
41.
SmithJ., & ToddP. (2005). Does matching overcome LaLonde’s critique of nonexperimental estimators?Journal of Econometrics, 125(1–2), 303–353.
42.
TakahashiK., ManoY., & OtsukaK. (2019). Learning from experts and peer farmers about rice production: Experimental evidence from Cote d’Ivoire. World Development, 122, 157–169.
43.
TakahashiK., MuraokaR., & OtsukaK. (2020). Technology adoption, impact and extension in developing countries’ agriculture: A review of the recent literature. Agricultural Economics, 51, 31–45.
44.
TamboJ. A., & MockshellJ. (2018). Differential impacts of conservation agriculture technology options on household income in sub-Saharan Africa. Ecological Economics, 151, 95–105.
45.
ThirtleC., LinL., & PlesseJ. (2003). The impact of research-led agricultural productivity growth on poverty reduction in Africa, Asia and Latin America. World Development, 31(12), 1959–1975.
46.
UNFCCC. (2006). Technologies for adaptation to climate change. United Nations Framework Convention on Climate Change.
47.
WaniS. P., ChnaderG., & AnanthaK. H. (2017). Enhancing resource use efficiency through soil management for improving livelihoods. In RakshitA., AbhilashP., SinghH. & GhoshS.Adaptive soil management: From theory to practice (pp. 413–451). Springer.
48.
WooldridgeJ. M. (2010). Econometric analysis of cross-section and panel data (2nd ed). MIT Press.
