Factors Promoting Utilisation of Climate-Smart Agricultural Practices in Northern Ghana
Authors
Seidu Nanundow
Department of Food Security and Climate Change, Faculty of Food and Consumer Sciences, University for Development Studies (UDS), Tamale, Ghana. (GH)
Hudu Zakaria
Department of Agricultural Innovation Communication, Faculty of Food and Consumer Sciences, University for Development Studies (UDS), Tamale, Ghana (GH)
Osman Tahidu Damba
Department of Agricultural and Food Economics, Faculty of Food and Consumer Sciences, University for Development Studies (UDS), Tamale, Ghana (GH)
Samuel Allottey
Department of Agricultural Innovation Communication, Faculty of Food and Consumer Sciences, University for Development Studies (UDS), Tamale, Ghana (GH)
Article Information
DOI: 10.51583/IJLTEMAS.2025.1410000119
Subject Category: AGRICULTURE
Volume/Issue: 14/10 | Page No: 980-991
Publication Timeline
Submitted: 2025-11-17
Published: 2025-11-17
Abstract
Abstract: This study examined the factors driving the adoption of Climate Smart Agricultural (CSA) practices in Northern Ghana. In nine selected communities, 318 smallholder farmers were interviewed, comprising 115 adopters and 203 non-adopters of CSA practices. Descriptive statistics were used to analyse the most common CSA practices in the area. Chi-square analysis and binary logistic regression were performed to examine which factors most likely influenced the use of the selected CSA practices. The study found that age, educational level, household size, access to extension and farmer-based organisations (FBO) membership of all smallholder farmers surveyed directly influenced smallholder farmers’ utilisation of CSA practices. As revealed in the study, up to 90% of the smallholder farmers in the study area were aware of the various CSA practices being examined. The study also showed that smallholder farmers frequently used on-farm composting, with the Chi-square test showing that access to extension services, household size, and educational attainment all had an impact on smallholder farmers' usage of on-farm composting. The study recommends that the Ministry of Food and Agriculture (MoFA) intensify extension service delivery on CSA practices to promote their adoption by smallholder farmers in the face of climate variability and change.
Keywords
Climate Change, Climate Smart Agriculture, Utilisation, Greenhouse Gas and Smallholder
Downloads
References
1. Abegunde, V.O., Sibanda, M., Obi, A. (2020). Determinants of the adoption of climate-smart agricultural practices by small-scale farming households in King Cetshwayo District Municipality, South Africa. Sustainability 12 (1), 195. [Google Scholar] [Crossref]
2. Abegunde, V. O., Sibanda, M., & 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(1), 195. [Google Scholar] [Crossref]
3. Agbenyo, W., Jiang, Y., Jia, X., Wang, J., Ntim-Amo, G., Dunya, R., ... & Twumasi, M. A. (2022). Does the Adoption of Climate-Smart Agricultural Practices Impact Farmers’ Income? Evidence from Ghana. International Journal of Environmental Research and Public Health, 19(7), 3804. [Google Scholar] [Crossref]
4. Andersson, J. A., & D'Souza, S. (2014). From adoption claims to understanding farmers and contexts: A literature review of Conservation Agriculture (CA) adoption among smallholder farmers in southern Africa. Agriculture, ecosystems & environment, 187, 116-132. [Google Scholar] [Crossref]
5. Agrawal, A. (2008). The Role of Local Institutions in Adaptation to Climate Change. Washington DC: Social Development Department, The World Bank. [Google Scholar] [Crossref]
6. Akram, M. W., Akram, N., Hongshu, W., Andleeb, S., ur Rehman, K., Kashif, U., & Mehmood, A. (2019). Impact of land use rights on the investment and efficiency of organic farming. Sustainability, 11(24), 7148. [Google Scholar] [Crossref]
7. Ambler, K., Herskowitz, S., & Maredia, M. (2020). Are we done yet? Response fatigue and rural livelihoods. IFPRI Discussion Paper 1980. Washington, DC: International Food Policy Research Institute (IFPRI). https://doi.org/10.2499/p15738coll2.134183 [Google Scholar] [Crossref]
8. 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 Journal of Geography, 11(1), 124-139. [Google Scholar] [Crossref]
9. Batisani, N. (2012) Climate variability, yield instability and global recession: the multi-stressor to food security in Botswana, Climate and Development, 4:2, 129-140, DOI: 10.1080/17565529.2012.728129. [Google Scholar] [Crossref]
10. Baudron, F., Zingore, S., & Giller, K. E. (2017). Participatory research to develop locally adapted conservation agriculture systems in Malawi. Experimental Agriculture, 53(3), 427-443. [Google Scholar] [Crossref]
11. Beddington, J., Asadazzama, M., Clark, M., Fernández, A., Guillou, M., Jahn, M., Wakhungu, J. (2012). Achieving food security in the face of climate change. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), (March), 60. [Google Scholar] [Crossref]
12. Belay, A., Recha, J. W., Woldeamanuel, T., & Morton, J. F. (2017). Smallholder farmers’ adaptation to climate change and determinants of their adaptation decisions in the Central Rift Valley of Ethiopia. Agriculture & Food Security, 6(1), 1-13. [Google Scholar] [Crossref]
13. Below, T. B., Mutabazi, K. D., Kirschke, D., Franke, C., Sieber, S., Siebert, R., & Tscherning, K. (2012). Can farmers’ adaptation to climate change be explained by socio-economic household-level variables. Global environmental change, 22(1), 223-235. [Google Scholar] [Crossref]
14. Birner, R., & Resnick, D. (2010). The political economy of policies for smallholder agriculture. World Development, 38(10), 1442-1452. [Google Scholar] [Crossref]
15. Branca, G. & Perelli, C. (2020). ‘Clearing the air’: common drivers of climate-smart smallholder food production in Eastern and Southern Africa, Journal of Cleaner Production, Volume 270. https://doi.org/10.1016/j.jclepro.2020.121900. [Google Scholar] [Crossref]
16. Dethier, J. J., & Effenberger, A. (2012). Agriculture and development: A brief review of the literature. Economic systems, 36(2), 175-205. [Google Scholar] [Crossref]
17. Essegbey G, Totin E, Karbo N, Traoré PS, Zougmoré R. (2016). Assessment of climate change policy and institutional context: The case of Ghana. CCAFS Working Paper no. 164. Copenhagen, Denmark: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). [Google Scholar] [Crossref]
18. Fagariba, C.J.; Song, S.; Soule Baoro, S.K.G. (2018). Climate Change Adaptation Strategies and Constraints in Northern Ghana: Evidence of Farmers in Sissala West District. Sustainability, 10, 1484. https://doi.org/10.3390/su10051484 [Google Scholar] [Crossref]
19. FAO. (2021). Climate-smart agriculture case studies 2021 – Projects from around the world. Rome. [Google Scholar] [Crossref]
20. FAO. (2009). Food Security and Agricultural Mitigation in Developing Countries: Options for Capturing Synergies. Rome, Italy. [Google Scholar] [Crossref]
21. FAO. 2005. Global Forest Resources Assessment 2005, Progress towards sustainable forest management. Vol. 147 of FAO Forestry Paper. Food and Agriculture Organization of the United Nations, Rome. [Google Scholar] [Crossref]
22. GASIP, 2022. Ghana Agricultural Sector Programme (GASIP), Climate Change Resilience Component: Guiding Notes for CA Farmer Selection, Accra: Ghanaian Ministry of Food & Agriculture. [Google Scholar] [Crossref]
23. Ghana Statistical Service (2020) Multidimensional Poverty-Ghana. Ghana Statistical Service, Accra [Google Scholar] [Crossref]
24. https://statsghana.gov.gh/gssmain/fileUpload/pressrelease/ Multidimensional%20Poverty%20Ghana_Report.pdf. [Google Scholar] [Crossref]
25. Harvey CA, Chacón M, Donatti CI, Garen E, Hannah L, Andrade A et al (2014) limate- smart landscapes: opportunities and challenges for integrating adaptation and mitigation in tropical agriculture. Conserv Lett 7(2):77–90. https://doi.org/10. 1111/conl.12066 [Google Scholar] [Crossref]
26. IFAD, 2019. Ghana Agricultural Sector Investment Programme: Supervision Report,Rome: IFAD [Google Scholar] [Crossref]
27. 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. [Google Scholar] [Crossref]
28. Kamara, A. Y., Sanogo, D., Abdoulaye, T., & Place, F. (2019). Agroforestry and improved agricultural water management for climate-smart agriculture in the Sahel. Climate Risk Management, 25, 100199. [Google Scholar] [Crossref]
29. Kangogo, D., Dentoni, D., & Bijman, J. (2021). Adoption of climate‐smart agriculture among smallholder farmers: Does farmer entrepreneurship matter? Land Use Policy, 109, 105666. [Google Scholar] [Crossref]
30. Karg, H., Schmitter, P., Schreinemachers, P., & Yameogo, V. (2020). Impact of soil and water conservation on crop yields and erosion in flood-recession agriculture in Burkina Faso. Agricultural Systems, 183, 102854.Kassem, M., Khoiry, M. A. & Hamzah, N. (2020). Using Relative Importance Index Method for Developing Risk Map in Oil and Gas Construction Projects. Jurnal Kejuruteraan. 32. 85-97. 10.17576/jkukm-2020-32(3)-09. [Google Scholar] [Crossref]
31. Louangrath, P. (2013). Alpha and Beta Tests for type I and type II inferential errors determination in hypothesis testing. Available at SSRN 2332756. [Google Scholar] [Crossref]
32. MoFA. (2020). “2019 Agricultural Sector Progress Report (APR).” Presented at 2020 Agricultural Joint Sector Review (JCR). Accra, Ghana: Ministry of Food and Agriculture (MoFA). [Google Scholar] [Crossref]
33. MoFA. (2021). Ministry of Food and Agriculture reports. Kumbungu district. [Google Scholar] [Crossref]
34. MoFA. (2022). Ministry of Food and Agriculture reports. Kumbungu district. [Google Scholar] [Crossref]
35. Morgan, L. D. (1997). Focus Groups as Qualitative Research (Volume 16). Sage Publication. [Google Scholar] [Crossref]
36. Osuala, E. C., (2005). Introduction to Research Methodology (3rd.Edition). African First Publishers Limited, Onitsha. [Google Scholar] [Crossref]
37. Panneerselvam, R., (2004). Research Methodology. Prentice Hall, New Delhi. [Google Scholar] [Crossref]
38. Partey ST, Zougmoré RB, Ouédraogo M, Campbell BM (2018) Developing climate- smart agriculture to face climate variability in West Africa: challenges and lessons learnt. J Clean Prod. [Google Scholar] [Crossref]
39. Rahut D.B., Jeetendra P.A, Paswel M. (2021). Ex-ante adaptation strategies for climate challenges in sub-Saharan Africa: Macro and micro perspectives, Environmental Challenges,Volume 3, 2021, 100035, ISSN 2667-0100, https://doi.org/10.1016/j.envc.2021.100035. [Google Scholar] [Crossref]
40. Rosenstock, T. S., Lamanna, C., Namoi, N., Arslan, A., and Richards, M. (2019). “What is the evidence base for climate-smart agriculture in East and Southern Africa? A systematic map,” in The Climate-Smart Agriculture Papers, eds T. Rosenstock, A. Nowak, E. Girvetz (Cham: Springer), 141–151 [Google Scholar] [Crossref]
41. Sova, C. A., Barron, J., Brahmbhatt, R., & Richardson, R. (2017). Advancing climate-smart agriculture in Mali through participatory research. The Journal of Agricultural Education and Extension, 23(4), 295-312. [Google Scholar] [Crossref]
42. Sim, J. (1998). Collecting and analysing qualitative data: issues raised by the focus group. Journal of advanced nursing, 28(2), 345-352. [Google Scholar] [Crossref]
43. Vanlauwe, B., Van Asten, P., Blomme, G., Mukalama, J., & Njoroge, S. M. C. (2014). Sustainable intensification and the African smallholder farmer. Current Opinion in Environmental Sustainability, 8, 15-22. [Google Scholar] [Crossref]
44. Yelfaanibe, A., Zackaria, S. G., Kanton, I. O. (2011). How is Integrated Water Resources Management Working at the Local Level? Perspectives from the Black Volta Basin of the Lawra District, Ghana. Journal of Environment and Earth Science. Vol.4, No.16. [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Decision Tree and Automatic Linear Modeling Approaches to Predict Body Weight in Indigenous Sabi Sheep and Matebele Goat Females of Zimbabwe
- Microcontroller – Based Automatic Railway Crossing Control and Track Obstacle Monitoring System
- Modelling the Role of Absorptive Capacity in Foreign Direct Investment - Economic Growth Nexus: A Focus on South Africa’s Manufacturing Sector
- AI-Powered Wristband for Accurate BAC Monitoring Using Smart Data Fusion
- Climate Change Impacts on Different Regions