Al-Qadisiyah Journal For Agriculture Sciences

What Impact Does the Uptake of Climate-Smart Agricultural Practices have on Rural Household Income? Evidence from Nigeria

Document Type : Research Paper

Authors

Abdulrazaq Kamal Daudu 1
Latifat Kehinde Olatinwo 2
Oyedola Waheed Kareem 1
Habeeb Kayode Yusuf 1
Olashile Mojeed Adeyemi 1

1 Department of Agricultural Extension and Rural Development, Faculty of Agriculture, University of Ilorin, Ilorin, Nigeria

2 Department of Agricultural Economics and Extension, Kwara State University, Malete, Nigeria

https://doi.org/10.33794/qjas.2023.139788.1126
Abstract
Recent studies have verified the importance of adopting CSA practices to reduce greenhouse gases (GHGs), combat climate change, and boost food security and farmers welfare. However, there have been few studies that have examined the causal impact of CSA practices on household income. This paper assesses the impact of adoption of CSA practices on farming households’ income in Northern Nigeria. Our sample consists of cross-sectional data of 480 (160 adopters and 320 non-adopters of CSA) rural farming households selected using randomize control trial (RCT) from three Northern States in Nigeria. This study employed propensity score matching (PSM) to establish the causal effect of adoption of CSA on households’ income while inverse probability-weighted regression adjustment (IPWRA) was used to controlled for selection bias that may arise from both observed and unobserved factors. We found that, age, education, farm size, access to extension, membership of association, and access to climatic information are positive and statistically significant influencing adoption of CSA practices among farming households. The empirical findings revealed that adoption significantly impacts the farming households’ income across the two estimators used. This highlights the importance of promoting adoption of CSA practices among rural farming households. Our findings emphasize that enlightenment campaign on CSA practices, access to extension and climate information, education of farming households, the size of farmland cultivated and group formation should be promoted in order to scale up its adoption and increase households’ income.

Keywords

Adoption,,
,،؛Climate-smart agriculture,,
,،؛matching technique,,
,،؛household income,,
,،؛rural,,
,،؛practices
Subjects
  1. IPCC (2014) 5th Assessment, reports available at https://www.ipcc.ch/report/ar5/
  2. Comoé, H. & Siegrist, M. (2015). Relevant drivers of farmers’ decision behavior regarding their adaptation to climate change: a case study of two regions in Côte d’Ivoire. Mitigation and Adaptation Strategies for Global Change, 20(2), 179-199. https://doi.org/10.1007/s11027-013-9486-7
  3. Arshad, A., Lindner, M., & Lehtonen, M. (2018). An Analysis of Photo-Voltaic Hosting Capacity in Finnish Low Voltage Distribution Networks. Energies 2017, 10, 1702," Energies, MDPI, vol. 11(3), pages 1-1.https://doi.org/10.3390/en10111702
  4. Hossain, M.S.; Arshad, M.; Qian, L.; Zhao, M.; Mehmood, Y.; Kächele, H. (2019). Economic impact of climate change on crop farming in Bangladesh: An application of Ricardian method. Ecol. Econ., 164, 106354.https://doi.org/10.1016/j.ecolecon.2019.106354
  5. Anuga, S.W.; Gordon, C.; Boon, E.; Surugu, J.M.-I. (2019). Determinants of climate smart agriculture (CSA) adoption among smallholder food crop farmers in the Techiman Municipality, Ghana. Ghana J. Geogr., 11, 124–139.
  6. (2018). Investment Framework for Mobilization Ofresources into Climate-Smart Agriculture (CSA) in Ghana; FAO: Accra, Ghana, 2018.
  7. Martey, E., Etwire, P. M., & Mockshell, J. (2021). Climate-smart cowpea adoption and welfare effects of comprehensive agricultural training programs. Technology in society, 64, 101468.https://doi.org/10.1016/j.techsoc.2020.101468
  8. Hansen, J., M. Sato, P. Kharecha, K. von Schuckmann, D.J. Beerling, J. Cao, S. Marcott, V. Masson Delmotte, M.J. Prather, E.J. Rohling, J. Shakun, P. Smith, A. Lacis, G. Russell, and R. Ruedy, (2017): Young people's burden: Requirement of negative CO2 emissions. Earth Syst. Dyn., 8, 577-616, doi:10.5194/esd-8-577-2017.https://doi.org/10.5194/esd-8-577-2017
  9. Amadu, F. O., McNamara, P. E., and Miller, D. C. (2020). Understanding the adoption of climate-smart agriculture: a farm-level typology with empirical evidence from southern Malawi. World Development, 126, 104692.https://doi.org/10.1016/j.worlddev.2019.104692
  10. Marenya, P. P., Gebremariam, G., Jaleta, M., and Rahut, D. B. (2020). Sustainable intensification among smallholder maize farmers in Ethiopia: adoption and impacts under rainfall and unobserved heterogeneity. Food Policy, 95, 101941. https://doi.org/10.1016/j.foodpol.2020.101941
  11. Tesfaye, W., G. Blalock, and N. Tirivayi. (2020). “Climate -Smart Innovations and Rural Poverty in Ethiopia: Exploring Impacts and Pathways.” American Journal of Agricultural Economics. https://doi.org/10.1111/ajae.12161
  12. Cramer, L.K. (2019). Access to early generation seed: Obstacles for delivery of climate-smart varieties. In The Climate-Smart Agriculture Papers; Springer: Cham, Switzerland; pp. 87–98. https://doi.org/10.1007/978-3-319-92798-5_8
  13. Komba, C., and Muchapondwa, E. (2018) Adaptation to climate change by smallholder farmers in Tanzania. Agricultural adaptation to climate change in Africa pp. 129-168, Routledge. https://doi.org/10.4324/9781315149776-7
  14. Adhikari, U., Nejadhashemi, A. P., & Woznicki, S. A. (2015). Climate change and eastern Africa: A review of impact on major crops. Food and Energy Security, 4(2), 110—132 https://doi.org/10.1002/fes3.61
  15. Okumu, B., Ntuli, H., Muchapondwa, E., Mudiriza, G., and Mukong, A. (2022). Does the uptake of multiple climate smart agriculture practices enhances household savings, food security and household vulnerability to climate change? Insights from Zimbabwe. ERSA working paper 870. January 2022.
  16. Lipper, L.; Thornton, P.; Campbell, B.M.; Baedeker, T.; Braimoh, A.; Bwalya, M.; Caron, P.; Cattaneo, A.; Garrity, D.; Henry, K.; et al. (2014). Climate-smart agriculture for food security. Nat. Clim. Chang., 4, 1068–1072. https://doi.org/10.1038/nclimate2437
  17. Imran, M.A.; Ali, A.; Ashfaq, M.; Hassan, S.; Culas, R.; Ma, C. (2019). Impact of climate smart agriculture (CSA) through sustainable irrigation management on Resource use efficiency: A sustainable production alternative for cotton. Land Use Policy, 88, 104113. https://doi.org/10.1016/j.landusepol.2019.104113
  18. Diko, S.K. (2018). Toward integration: Managing the divergence between national climate change interventions and urban planning in Ghana. In Smart, Resilient and Transition Cities: Emerging Approaches and Tools for a Climate-Sensitive Urban Development; Elsevier: Amsterdam, The Netherlands; pp. 141–152. https://doi.org/10.3390/ifou2018-06035
  19. National Development Planning Commission (2017). Medium-Term National Development Policy Framework—An Agenda for Jobs: Creating Prosperity and Equal Opportunity for All (First Step) 2018–2021; Government of Ghana, National Development Planning Commission: Accra, Ghana, 2017.
  20. Wekesa, B. M., Ayuya, O. I., & Lagat, J. K. (2018). Effect of climate-smart agricultural practices on household food security in smallholder production systems: micro-level evidence from Kenya. Agriculture & Food Security, 7(1), 80. https://doi.org/10.1186/s40066-018-0230-0
  21. Teklewold, H., Gebrehiwot, T., Bezabih, M., (2019). Climate smart agricultural practices and gender differentiated nutrition outcome: Empirical evidence from Ethiopia. World Dev 122, 38—53. https://doi.org/10.1016/j.worlddev.2019.05.010
  22. Makate, C., Makate, M., Mango, N., Siziba, S., (2019). Increasing resilience ofsmallholder farmers to climate change through multiple adoption of proven climate-smart agriculture innovations. Lessons from Southern Africa. J. Environ. Manage. 231, 858—868. https://doi.org/10.1016/j.jenvman.2018.10.069
  23. Tibesigwa, B., H. Ntuli and Lokina, and C. Komba, (2019). Long-rains crops, short rains crops, permanent crops and fruit crops: the ’hidden’ multiple season-cropping system for adaptation to rain variability by smallholder farms. Journal of Environmental Management, Volume 278, Part 2, 111407. https://doi.org/10.1016/j.jenvman.2020.111407
  24. Liang, Z., Zhang, L., Li, W., Zhang, J., & Frewer, L. (2021). Adoption of combinations of adaptive and mitigatory climate-smart agricultural practices and its impacts on rice yield and income: Empirical evidence from Hubei, China. Climate Risk Management, 100314.
    https://doi.org/10.1016/j.crm.2021.100314
  25. Ogada, M.J., Rao, E.J.O., Radeny, M., Recha, J.W., Solomon, D., (2020). Climate-smart agriculture, household income and asset accumulation among smallholder farmers in the Nyando basin of Kenya. World Dev. Perspect. 18, 100203. https://doi.org/10.1016/j.wdp.2020.100203
  26. Fentie, A., & Beyene, A. D. (2019). Climate-smart agricultural practices and welfare of rural smallholders in Ethiopia: Does planting method matter? Land use policy, 85, 387-396.
    https://doi.org/10.1016/j.landusepol.2019.04.020
  27. Wooldridge, J.M (2010). Econometric Analysis of Cross Section and Panel Data, 2nd ed.; MIT Press: Cambridge, MA, USA.
  28. Imbens, G. W., & Wooldridge, J. W. (2009). Recent developments in the econometrics of program development. Journal of Economic Literature, 47(1), 5–86. https://doi.org/10.1257/jel.47.1.5
  29. Robins, J. M., & Rotnitzky, A. (1995). Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association, 90(429), 122–129. https://doi.org/10.1080/01621459.1995.10476494
  30. Nilsson, N. (2017). Productivity effects of CAP investment support: Evidence from Sweden using matched panel data. Land Use Policy, 66, 172–182. https://doi.org/10.1016/j.landusepol.2017.04.043
  31. Michalek, J.; Ciaian, P.; and Kancs, D. (2016). Investment crowding out: Firm-level evidence from northern Germany. Reg. Stud., 50, 1579–1594. https://doi.org/10.1080/00343404.2015.1044957
  32. Hoken, H.; and Su, Q. (2015). Measuring the effect of Agricultural Cooperatives on Household Income using PSM-DID: A case study of Rice-Producing Cooperative in China. In Disscussion Paper No. 539; Institute of Developing Economies, Japan External Trade Organization: Chiba, Japan, 2015. https://doi.org/10.1002/agr.21554
  33. Abebaw, D.; and Haile, M.G. (2013). The impact of cooperatives on agricultural technology adoption: Empirical evidence from Ethiopia. Food Policy, 38, 82–91. https://doi.org/10.1016/j.foodpol.2012.10.003
  34. Ito, J.; Bao, Z.; and Suc, Q. (2012). Distributional effects of agricultural cooperatives in China: Exclusion of smallholders and potential gains on participation. Food Policy, 37, 700–709. https://doi.org/10.1016/j.foodpol.2012.07.009
  35. Démurger, S.; and Haiyuan Wan, H. (2012). Payments for Ecological Restoration and Internal Migration in China: The Sloping Land Conversion Program in Ningxia, No. 1233; Working Papers; Groupe d’Analyse et de Théorie Economique Lyon St-Étienne: Lyon, France, 2012. https://doi.org/10.2139/ssrn.2185280
  36. Gautam, S.; Schreinemachers, P.; Uddin, N. (2017). Impact of training vegetable farmers in Bangladesh in integrated pest management (IPM). Crop Prot., 102, 161–169. https://doi.org/10.1016/j.cropro.2017.08.022
  37. Schreinemachers, P.; Wu, M.-H.; Uddin, M.N.; Ahmad, S.; Hanson, P. (2016). Farmer training in off-season vegetables: Effects on income and pesticide use in Bangladesh. Food Policy, 61, 132–140. https://doi.org/10.1016/j.foodpol.2016.03.002
  38. Fischer, E.; Qaim, M. (2012). Linking smallholders to markets: Determinants and impacts of farmer collective action in Kenya. World Dev., 40, 1255–1268. https://doi.org/10.1016/j.worlddev.2011.11.018
  39. Diagne, A., and Demont, M. (2007). Taking a new look at empirical models of adoption: Average treatment effect estimation of adoption rate and its determinants. Agric. Econ., 37, 2–3. https://doi.org/10.1111/j.1574-0862.2007.00266.x
  40. Khandker, S. R., Koolwal, G. B., & Samad, H. A. (2010). Handbook on impact evaluation: quantitative methods and practices. World Bank Publications. https://doi.org/10.1596/978-0-8213-8028-4
  41. Rosenbaum, R.P., and Rubin, B.D (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am. Stat., 39, 33–38. https://doi.org/10.1080/00031305.1985.10479383
  42. Heckman, J.J., LaLonde, R.J., Smith, J.A. (1999). The economics and econometrics of active labour market programs. Handbook of Labour Economics. 1999; 3:1865–2097 https://doi.org/10.1016/s1573-4463(99)03012-6
  43. Caliendo, M., and Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. J. Econ. Surv., 22, 31–72. https://doi.org/10.1111/j.1467-6419.2007.00527.x
  44. Sianesi, B. (2004). An evaluation of the Swedish system of active labour market programs in the 1990s. Rev Econ Stat. 2004;86(1):133–55. https://doi.org/10.1162/003465304323023723
  45. Wossen, T., Alene, A., and Abdoulaye, T. (2019). Poverty Reduction Effects of Agricultural Technology Adoption: The Case of Poverty Reduction Effects of Agricultural Technology Adoption: The Case of Improved Cassava Varieties in Nigeria. J. Agric. Econ., 70, 392–407. https://doi.org/10.1111/1477-9552.12296
  46. Varma, P. (2019). Adoption and the impact of system of rice intensification on rice yields and household income: an analysis for India. Appl Econ. 2019;51(45):4956–72. https://doi.org/10.1080/00036846.2019.1606408
  47. Bello, L.O.; Baiyegunhi, L.J.S.; Danso-Abbeam, G (2020). Productivity impact of improved rice varieties’ adoption: Case of smallholder rice farmers in Nigeria. Econ. Innov. New Technol. 2020, 1–17. https://doi.org/10.1080/10438599.2020.1776488
  48. Yirga, C., Bekele, A., & Kassie, M. (2017). Adoption and diffusion of sustainable intensification practices for maize-legume production in Ethiopia: A panel data analysis, Ethiopian Institute of Agricultural Research (EIAR). (978-99944-66-46-7).
  49. Abegunde, V.O., Sibanda, M., and Obi, A. (2019). Determinants of the adoption of climate smart agricultural practices by small-scale farming households in king cetshwayo district municipality, South Africa. Sustainability; 12:195. https://doi.org/10.3390/su12010195
  50. Wordofa, M.G., Hassen, J.Y, Endris, G.S, Aweke, C.S., Moges, D.K., & Rorisa, D.T (2021). Adoption of improved agricultural technology and its impact on household income: a propensity score matching estimation in eastern Ethiopia. Agric & Food Secur (2021), 10:5. https://doi.org/10.1186/s40066-020-00278-2
  51. Habtemariam, L.T., Mgeni, C.P., Mutabazi, K.D., and Sieber, S. (2019). The fam income and food security implications of adopting fertilizer micro-dosing and tiedridge technologies under semi-arid environments in central Tanzania. J. Arid Environ. 2019;166(2019):60–7. https://doi.org/10.1016/j.jaridenv.2019.02.011
  52. Teklewold, H., Kassie, M., Shiferaw, B., and Köhlin, G. (2013). Cropping system diversification, conservation tillage and modern seed adoption in Ethiopia: Impacts on household income, agrochemical use and demand for labor. Ecol. Econ. 2013;93(2013):85–93. https://doi.org/10.1016/j.ecolecon.2013.05.002
  53. Hailu, B.K., Abrha, B.K., Weldegiorgis, K.A. (2014). Adoption and impact of agricultural technologies on farm income: evidence from southern Tigray, Northern Ethiopia. Int. J. Food Agric. Econ. 2014;2(4):91–106.
Al-Qadisiyah Journal For Agriculture Sciences
Volume 13, Issue 2
December 2023
Page 1-17

  • Receive Date 15 May 2023
  • Revise Date 19 June 2023
  • Accept Date 24 June 2023