Socioeconomic factors influencing adoption of climate-smart agriculture technologies by smallholder farmers in semi-arid areas, Tanzania

Paper Details

Research Paper 08/05/2025
Views (177) Download (29)
current_issue_feature_image
publication_file

Socioeconomic factors influencing adoption of climate-smart agriculture technologies by smallholder farmers in semi-arid areas, Tanzania

George Mbyazita Karwani, Mashamba Philipo, Akida I. Meya, Mamo A. Teshale
Int. J. Agron. Agri. Res.26( 5), 14-25, May 2025.
Certificate: IJAAR 2025 [Generate Certificate]

Abstract

Climate change poses significant challenges to agricultural productivity and food security, particularly in developing countries where agriculture remains a critical livelihood source. In response, climate-smart agriculture technologies (CSATs) have emerged as vital tools to enhance resilience and sustainability in farming systems. This study investigates the socioeconomic factors influencing the adoption of CSATs, with a focus on maize-common bean intercropping systems among smallholder farmers in the semi-arid districts of Singida Rural, Babati, and Kondoa in Tanzania. A mixed-methods approach was employed, combining a structured questionnaire survey with 240 smallholder farmers, focus group discussions, key informant interviews, and document reviews to ensure data triangulation. Quantitative data were analyzed using descriptive statistics and logistic regression through the Statistical Package for Social Sciences (SPSS). The results indicate that several socioeconomic variables significantly influence the adoption of maize-common bean intercropping as a CSAT. These include gender, age, level of education, household size, farm size, access to extension services, and availability of agricultural credit. Male-headed households and farmers with better access to information and resources were more likely to adopt CSATs. The findings underscore the need for policy frameworks and development interventions that address these critical socioeconomic barriers to adoption. Strengthening institutional support, improving access to extension and credit services, and enhancing farmer education and awareness are recommended to foster widespread adoption of CSATs. Ultimately, promoting inclusive adoption strategies to enhance agricultural resilience, improve food security, and contribute to sustainable rural livelihoods in Tanzania’s semi-arid regions.

Agarwal T, Goel PA, Gartaula H, Rai M, Bijarniya D, Rahut DB, Jat ML. 2022. Gendered impacts of climate-smart agriculture on household food security and labor migration: Insights from Bihar, India. International Journal of Climate Change Strategies and Management 14(1), 1–19. https://doi.org/10.1108/IJCCSM-01-2020-0004

Agbenyo W, Jiang Y, Jia X, Wang J, Ntim-Amo G, Dunya R, Siaw A, Asare I, Twumasi MA. 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). https://doi.org/10.3390/ijerph19073804

Ayinu YT, Ayal DY, Zeleke TT, Beketie KT. 2022. Impact of climate variability on household food security in Godere District, Gambella Region, Ethiopia. Climate Services 27. https://doi.org/10.1016/j.cliser.2022.100307

Balogun VS, Onokerhoraye AG. 2022. Climate change vulnerability mapping across ecological zones in Delta State, Niger Delta Region of Nigeria. Climate Services 27. https://doi.org/10.1016/j.cliser.2022.100304

Bett PE, Thornton HE, Troccoli A, De Felice M, Suckling E, Dubus L, Saint-Drenan YM, Brayshaw DJ. 2022. A simplified seasonal forecasting strategy, applied to wind and solar power in Europe. Climate Services 27. https://doi.org/10.1016/j.cliser.2022.100318

Bremer S, Bremer A, Iversen L, Bruno Soares M, van der Sluijs J. 2022. Recognising the social functions of climate services in Bergen, Norway. Climate Services 27. https://doi.org/10.1016/j.cliser.2022.100305

Etikan I. 2016. Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics 5(1), 1. https://doi.org/10.11648/j.ajtas.20160501.11

Hussein A. 2024. Climate smart agriculture strategies for enhanced agricultural resilience and food security under a changing climate in Ethiopia. Sustainable Environment 10(1). https://doi.org/10.1080/27658511.2024.2345433

Jones K, Nowak A, Berglund E, Grinnell W, Temu E, Paul B, Renwick LLR, Steward P, Rosenstock TS, Kimaro AA. 2023. Evidence supports the potential for climate-smart agriculture in Tanzania. Global Food Security 36. https://doi.org/10.1016/j.gfs.2022.100666

Kakzan D, Arslan A, Lipper L. 2013. Climate-smart agriculture. A review of current practice of agroforestry and conservation agriculture. www.fao.org/economic/esa

Kangalawe RYM, Lyimo JG. 2013. Climate change, adaptive strategies and rural livelihoods in semiarid Tanzania. Natural Resources 4(3), 266–278. https://doi.org/10.4236/nr.2013.43034

Kassa BA, Abdi AT. 2022. Factors influencing the adoption of climate-smart agricultural practice by small-scale farming households in Wondo Genet, Southern Ethiopia. SAGE Open 12(3). https://doi.org/10.1177/21582440221121604

Kombat R, Sarfatti P, Fatunbi OA. 2021. A review of climate-smart agriculture technology adoption by farming households in Sub-Saharan Africa. Sustainability 13(21). https://doi.org/10.3390/su132112130

Kurgat BK, Lamanna C, Kimaro A, Namoi N, Manda L, Rosenstock TS. 2020. Adoption of climate-smart agriculture technologies in Tanzania. Frontiers in Sustainable Food Systems 4(May). https://doi.org/10.3389/fsufs.2020.00055

Lee M. n.d. Summary of Durbin-Watson model. November 2021.

Luhunga PM, Kijazi AL, Chang’a L, Kondowe A, Ng’ongolo H, Mtongori H. 2018. Climate change projections for Tanzania based on high-resolution regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX)-Africa. Frontiers in Environmental Science 6(October), 1–20. https://doi.org/10.3389/fenvs.2018.00122

Mizik T. 2021. Climate-smart agriculture on small-scale farms: A systematic literature review. Agronomy 11(6). https://doi.org/10.3390/agronomy11061096

Mnukwa ML, Mdoda L, Mudhara M. 2025. Assessing the adoption and impact of climate-smart agricultural practices on smallholder maize farmers’ livelihoods in Sub-Saharan Africa: A systematic review. Frontiers in Sustainable Food Systems 9(February), 1–22. https://doi.org/10.3389/fsufs.2025.1543805

Mthethwa KN, Ngidi MSC, Ojo TO, Hlatshwayo SI. 2022. The determinants of adoption and intensity of climate-smart agricultural practices among smallholder maize farmers. Sustainability 14(24). https://doi.org/10.3390/su142416926

Musafiri CM, Kiboi M, Macharia J, Ng’etich OK, Kosgei DK, Mulianga B, Okoti M, Ngetich FK. 2022. Adoption of climate-smart agricultural practices among smallholder farmers in Western Kenya: Do socioeconomic, institutional, and biophysical factors matter? Heliyon 8(1), e08677. https://doi.org/10.1016/j.heliyon.2021.e08677

Negera M, Alemu T, Hagos F, Haileslassie A. 2022. Determinants of adoption of climate smart agricultural practices among farmers in Bale-Eco region, Ethiopia. Heliyon 8(7), e09824. https://doi.org/10.1016/j.heliyon.2022.e09824

Nkumulwa HO, Pauline NM. 2021. Role of climate-smart agriculture in enhancing farmers’ livelihoods and sustainable forest management: A case of villages around Songe-Bokwa Forest, Kilindi District, Tanzania. Frontiers in Sustainable Food Systems 5(August), 1–15. https://doi.org/10.3389/fsufs.2021.671419

Oppong E, Opoku A, Tuffour HO, Snr APP, Kyere CG. 2021. Climate change and climate-smart agricultural practices: Opportunities and challenges in the semi-deciduous region of Ghana. International Journal of Environment and Climate Change, 100–110. https://doi.org/10.9734/ijecc/2021/v11i630426

Pallant J. 2005. SPSS survival manual: A step-by-step guide to data analysis using SPSS for Windows (Version 12).

Partey ST, Zougmoré RB, Ouédraogo M, Thevathasan NV. 2017. Why promote improved fallows as a climate-smart agroforestry technology in Sub-Saharan Africa? Sustainability 9(11). https://doi.org/10.3390/su9111887

Rai N, Thapa B. 2019. A study on purposive sampling method in research. Kathmandu: Kathmandu School of Law, 1–12. http://stattrek.com/survey-research/sampling-methods.aspx?Tutorial=AP,%0Ahttp://www.academia.edu/28087388

Raji E, Ijomah TI, Eyieyien OG. 2024. Improving agricultural practices and productivity through extension services and innovative training programs. International Journal of Applied Research in Social Sciences 6(7), 1297–1309. https://doi.org/10.51594/ijarss.v6i7.1267

Schreuder HT, Gregoire TG, Weyer JP. 2001. For what applications can probability and non-probability sampling be used? Environmental Monitoring and Assessment 66(3), 281–291. https://doi.org/10.1023/A:1006316418865

Shen L, Wen J, Zhang Y, Ullah S, Cheng J, Meng X. 2022. Changes in population exposure to extreme precipitation in the Yangtze River Delta, China. Climate Services 27. https://doi.org/10.1016/j.cliser.2022.100317

Summary T. 2023. Technical summary. In Climate Change 2022- Mitigation of Climate Change. https://doi.org/10.1017/9781009157926.002

Thierfelder C, Chivenge P, Mupangwa W, Rosenstock TS, Lamanna C, Eyre JX. 2017. How climate-smart is conservation agriculture (CA)? – Its potential to deliver on adaptation, mitigation and productivity on smallholder farms in southern Africa. Food Security 9(3), 537–560. https://doi.org/10.1007/s12571-017-0665-3

Wadood F, Akbar F, Ullah I. 2021. The importance and essential steps of pilot testing in management studies: A quantitative survey results. Journal of Contemporary Issues in Business and Government 27(5), 2021.

Yusuph AS, Nzunda EF, Mourice SK, Dalgaard T. 2023. Usage of agroecological climate-smart agriculture practices among sorghum and maize smallholder farmers in semi-arid areas in Tanzania. East African Journal of Agriculture and Biotechnology 6(1), 378–406. https://doi.org/10.37284/eajab.6.1.1490